BioMed Research International: Computational Biology The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. Corrigendum to “Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns” Mon, 13 Nov 2017 00:00:00 +0000 Zhi Han, Travis Johnson, Jie Zhang, Xuan Zhang, and Kun Huang Copyright © 2017 Zhi Han et al. All rights reserved. Gene Prediction in Metagenomic Fragments with Deep Learning Wed, 08 Nov 2017 07:02:36 +0000 Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments. Shao-Wu Zhang, Xiang-Yang Jin, and Teng Zhang Copyright © 2017 Shao-Wu Zhang et al. All rights reserved. Computational Molecular Networks and Network Pharmacology Wed, 08 Nov 2017 00:00:00 +0000 Kang Ning, Xinming Zhao, Ansgar Poetsch, Wei-Hua Chen, and Jialiang Yang Copyright © 2017 Kang Ning et al. All rights reserved. RNA-seq Based Transcription Characterization of Fusion Breakpoints as a Potential Estimator for Its Oncogenic Potential Tue, 17 Oct 2017 00:00:00 +0000 Based on high-throughput sequencing technology, the detection of gene fusions is no longer a big challenge but estimating the oncogenic potential of fusion genes remains challenging. Recent studies successfully applied machine learning methods and gene structural and functional features of fusion mutation to predict their oncogenic potentials. However, the transcription characterizations features of fusion genes have not yet been studied. In this study, based on the clonal evolution theory, we hypothesized that a fusion gene is more likely to be an oncogenic genomic alteration, if the neoplastic cells harboring this fusion mutation have larger clonal size than other neoplastic cells in a tumor. We proposed a novel method, called iFCR (internal Fusion Clone Ratio), given an estimation of oncogenic potential for fusion mutations. We have evaluated the iFCR method in three public cancer transcriptome sequencing datasets; the results demonstrated that the fusion mutations occurring in tumor samples have higher internal fusion clone ratio than normal samples. And the most frequent prostate cancer fusion mutation, TMPRSS2-ERG, appears to have a remarkably higher iFCR value in all three independent patients. The preliminary results suggest that the internal fusion clone ratio might potentially advantage current fusion mutation oncogenic potential prediction methods. Jian-lei Gu, Morris Chukhman, Yao Lu, Cong Liu, Shi-yi Liu, and Hui Lu Copyright © 2017 Jian-lei Gu et al. All rights reserved. Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach Sun, 15 Oct 2017 07:08:46 +0000 Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. Fan Liang, Weihong Xie, and Yang Yu Copyright © 2017 Fan Liang et al. All rights reserved. All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds Sun, 08 Oct 2017 00:00:00 +0000 Recent advances in understanding protein folding have benefitted from coarse-grained representations of protein structures. Empirical energy functions derived from these techniques occasionally succeed in distinguishing native structures from their corresponding ensembles of nonnative folds or decoys which display varying degrees of structural dissimilarity to the native proteins. Here we utilized atomic coordinates of single protein chains, comprising a large diverse training set, to develop and evaluate twelve all-atom four-body statistical potentials obtained by exploring alternative values for a pair of inherent parameters. Delaunay tessellation was performed on the atomic coordinates of each protein to objectively identify all quadruplets of interacting atoms, and atomic potentials were generated via statistical analysis of the data and implementation of the inverted Boltzmann principle. Our potentials were evaluated using benchmarking datasets from Decoys-‘R’-Us, and comparisons were made with twelve other physics- and knowledge-based potentials. Ranking 3rd, our best potential tied CHARMM19 and surpassed AMBER force field potentials. We illustrate how a generalized version of our potential can be used to empirically calculate binding energies for target-ligand complexes, using HIV-1 protease-inhibitor complexes for a practical application. The combined results suggest an accurate and efficient atomic four-body statistical potential for protein structure prediction and assessment. Majid Masso Copyright © 2017 Majid Masso. All rights reserved. Model Checking Temporal Logic Formulas Using Sticker Automata Thu, 28 Sep 2017 00:00:00 +0000 As an important complex problem, the temporal logic model checking problem is still far from being fully resolved under the circumstance of DNA computing, especially Computation Tree Logic (CTL), Interval Temporal Logic (ITL), and Projection Temporal Logic (PTL), because there is still a lack of approaches for DNA model checking. To address this challenge, a model checking method is proposed for checking the basic formulas in the above three temporal logic types with DNA molecules. First, one-type single-stranded DNA molecules are employed to encode the Finite State Automaton (FSA) model of the given basic formula so that a sticker automaton is obtained. On the other hand, other single-stranded DNA molecules are employed to encode the given system model so that the input strings of the sticker automaton are obtained. Next, a series of biochemical reactions are conducted between the above two types of single-stranded DNA molecules. It can then be decided whether the system satisfies the formula or not. As a result, we have developed a DNA-based approach for checking all the basic formulas of CTL, ITL, and PTL. The simulated results demonstrate the effectiveness of the new method. Weijun Zhu, Changwei Feng, and Huanmei Wu Copyright © 2017 Weijun Zhu et al. All rights reserved. Systems Study on the Antirheumatic Mechanism of Tibetan Medicated-Bath Therapy Using Wuwei-Ganlu-Yaoyu-Keli Tue, 26 Sep 2017 00:00:00 +0000 In clinical practice at Tibetan area of China, Traditional Tibetan Medicine formula Wuwei-Ganlu-Yaoyu-Keli (WGYK) is commonly added in warm water of bath therapy to treat rheumatoid arthritis (RA). However, its mechanism of action is not well interpreted yet. In this paper, we first verify WGYK’s anti-RA effect by an animal experiment. Then, based on gene expression data from microarray experiments, we apply approaches of network pharmacology to further reveal the mechanism of action for WGYK to treat RA by analyzing protein-protein interactions and pathways. This study may facilitate our understanding of anti-RA effect of WGYK from perspective of network pharmacology. Tianhong Wang, Jian Yang, Xing Chen, Kehui Zhao, Jing Wang, Yi Zhang, Jing Zhao, and Yang Ga Copyright © 2017 Tianhong Wang et al. All rights reserved. Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis Mon, 25 Sep 2017 00:00:00 +0000 Chloride intracellular channel 1 (CLIC1) is involved in the development of most aggressive human tumors, including gastric, colon, lung, liver, and glioblastoma cancers. It has become an attractive new therapeutic target for several types of cancer. In this work, we aim to identify natural products as potent CLIC1 inhibitors from Traditional Chinese Medicine (TCM) database using structure-based virtual screening and molecular dynamics (MD) simulation. First, structure-based docking was employed to screen the refined TCM database and the top 500 TCM compounds were obtained and reranked by -Score. Then, 30 potent hits were achieved from the top 500 TCM compounds using cluster and ligand-protein interaction analysis. Finally, MD simulation was employed to validate the stability of interactions between each hit and CLIC1 protein from docking simulation, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis was used to refine the virtual hits. Six TCM compounds with top MM-GBSA scores and ideal-binding models were confirmed as the final hits. Our study provides information about the interaction between TCM compounds and CLIC1 protein, which may be helpful for further experimental investigations. In addition, the top 6 natural products structural scaffolds could serve as building blocks in designing drug-like molecules for CLIC1 inhibition. Wei Wang, Minghui Wan, Dongjiang Liao, Guilin Peng, Xin Xu, Weiqiang Yin, Guixin Guo, Funeng Jiang, Weide Zhong, and Jianxing He Copyright © 2017 Wei Wang et al. All rights reserved. Systematic Standardized and Individualized Assessment of Masticatory Cycles Using Electromagnetic 3D Articulography and Computer Scripts Mon, 18 Sep 2017 00:00:00 +0000 Masticatory movements are studied for decades in odontology; a better understanding of them could improve dental treatments. The aim of this study was to describe an innovative, accurate, and systematic method of analyzing masticatory cycles, generating comparable quantitative data. The masticatory cycles of 5 volunteers (Class I, 19 ± 1.7 years) without articular or dental occlusion problems were evaluated using 3D electromagnetic articulography supported by MATLAB software. The method allows the trajectory morphology of the set of chewing cycles to be analyzed from different views and angles. It was also possible to individualize the trajectory of each cycle providing accurate quantitative data, such as number of cycles, cycle areas in frontal view, and the ratio between each cycle area and the frontal mandibular border movement area. There was a moderate negative correlation (−0.61) between the area and the number of cycles: the greater the cycle area, the smaller the number of repetitions. Finally it was possible to evaluate the area of the cycles through time, which did not reveal a standardized behavior. The proposed method provided reproducible, intelligible, and accurate quantitative and graphical data, suggesting that it is promising and may be applied in different clinical situations and treatments. Ramón Fuentes, Alain Arias, María Florencia Lezcano, Diego Saravia, Gisaku Kuramochi, and Fernando José Dias Copyright © 2017 Ramón Fuentes et al. All rights reserved. A Multiorgan Segmentation Model for CT Volumes via Full Convolution-Deconvolution Network Sun, 17 Sep 2017 08:03:30 +0000 We propose a model with two-stage process for abdominal segmentation on CT volumes. First, in order to capture the details of organs, a full convolution-deconvolution network (FCN-DecNet) is constructed with multiple new unpooling, deconvolutional, and fusion layers. Then, we optimize the coarse segmentation results of FCN-DecNet by multiscale weights probabilistic atlas (MS-PA), which uses spatial and intensity characteristic of atlases. Our coarse-fine model takes advantage of intersubject variability, spatial location, and gray information of CT volumes to minimize the error of segmentation. Finally, using our model, we extract liver, spleen, and kidney with Dice index of 90.1 ± 1%, 89.0 ± 1.6%, and 89.0 ± 1.3%, respectively. Yangzi Yang, Huiyan Jiang, and Qingjiao Sun Copyright © 2017 Yangzi Yang et al. All rights reserved. Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining Sun, 30 Jul 2017 00:00:00 +0000 Background and Objective. Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. Methods. On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits. Results. In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines. Conclusions. The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future. Jianxiao Liu and Zonglin Tian Copyright © 2017 Jianxiao Liu and Zonglin Tian. All rights reserved. Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns Mon, 17 Jul 2017 00:00:00 +0000 We presented a novel workflow for detecting distribution patterns in cell populations based on single-cell transcriptome study. With the fast adoption of single-cell analysis, a challenge to researchers is how to effectively extract gene features to meaningfully separate the cell population. Considering that coexpressed genes are often functionally or structurally related and the number of coexpressed modules is much smaller than the number of genes, our workflow uses gene coexpression modules as features instead of individual genes. Thus, when the coexpressed modules are summarized into eigengenes, not only can we interactively explore the distribution of cells but also we can promptly interpret the gene features. The interactive visualization is aided by a novel application of spatial statistical analysis to the scatter plots using a clustering index parameter. This parameter helps to highlight interesting 2D patterns in the scatter plot matrix (SPLOM). We demonstrated the effectiveness of the workflow using two large single-cell studies. In the Allen Brain scRNA-seq dataset, the visual analytics suggested a new hypothesis such as the involvement of glutamate metabolism in the separation of the brain cells. In a large glioblastoma study, a sample with a unique cell migration related signature was identified. Zhi Han, Travis Johnson, Jie Zhang, Xuan Zhang, and Kun Huang Copyright © 2017 Zhi Han et al. All rights reserved. DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction Tue, 04 Jul 2017 08:26:37 +0000 Background. Drug-target interaction is key in drug discovery, especially in the design of new lead compound. However, the work to find a new lead compound for a specific target is complicated and hard, and it always leads to many mistakes. Therefore computational techniques are commonly adopted in drug design, which can save time and costs to a significant extent. Results. To address the issue, a new prediction system is proposed in this work to identify drug-target interaction. First, drug-target pairs are encoded with a fragment technique and the software “PaDEL-Descriptor.” The fragment technique is for encoding target proteins, which divides each protein sequence into several fragments in order and encodes each fragment with several physiochemical properties of amino acids. The software “PaDEL-Descriptor” creates encoding vectors for drug molecules. Second, the dataset of drug-target pairs is resampled and several overlapped subsets are obtained, which are then input into kNN (-Nearest Neighbor) classifier to build an ensemble system. Conclusion. Experimental results on the drug-target dataset showed that our method performs better and runs faster than the state-of-the-art predictors. Jinjian Jiang, Nian Wang, Peng Chen, Jun Zhang, and Bing Wang Copyright © 2017 Jinjian Jiang et al. All rights reserved. Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement Wed, 28 Jun 2017 00:00:00 +0000 Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect. Fei Zhou, ZhenHong Jia, Jie Yang, and Nikola Kasabov Copyright © 2017 Fei Zhou et al. All rights reserved. Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis Thu, 15 Jun 2017 10:59:55 +0000 Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatics method to analyze the dysregulated interaction network of microRNA-mRNA in NSCLC, based on both the gene expression data and the microRNA-gene regulation network. Considering the properties of the substructure and their biological functions, we identified the putative diagnostic biomarker microRNAs, some of which have been reported on the PubMed citations while the rest, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were predicted as the putative novel microRNA biomarker for the diagnosis of NSCLC adenocarcinoma. They were further validated by functional enrichment analysis of their target genes. These findings deserve further experimental validations for future clinical application. Yang Shao, Bin Liang, Fei Long, and Shu-Juan Jiang Copyright © 2017 Yang Shao et al. All rights reserved. Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER Tue, 13 Jun 2017 06:21:32 +0000 Current multiomics assay platforms facilitate systematic identification of functional entities that are mappable in a biological network, and computational methods that are better able to detect densely connected clusters of signals within a biological network are considered increasingly important. One of the most famous algorithms for detecting network subclusters is Molecular Complex Detection (MCODE). MCODE, however, is limited in simultaneous analyses of multiple, large-scale data sets, since it runs on the Cytoscape platform, which requires extensive computational resources and has limited coding flexibility. In the present study, we implemented the MCODE algorithm in R programming language and developed a related package, which we called MCODER. We found the MCODER package to be particularly useful in analyzing multiple omics data sets simultaneously within the R framework. Thus, we applied MCODER to detect pharmacologically tractable protein-protein interactions selectively elevated in molecular subtypes of ovarian and colorectal tumors. In doing so, we found that a single molecular subtype representing epithelial-mesenchymal transition in both cancer types exhibited enhanced production of the collagen-integrin protein complex. These results suggest that tumors of this molecular subtype could be susceptible to pharmacological inhibition of integrin signaling. Sungjin Kwon, Hyosil Kim, and Hyun Seok Kim Copyright © 2017 Sungjin Kwon et al. All rights reserved. Analysis and Modeling for Big Data in Cancer Research Mon, 12 Jun 2017 06:13:45 +0000 Bing Niu, Peter B. Harrington, Guozheng Li, Jianxin Li, and Simon Poon Copyright © 2017 Bing Niu et al. All rights reserved. Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network Mon, 05 Jun 2017 07:26:13 +0000 MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks. Yuming Zhao, Fang Wang, Su Chen, Jun Wan, and Guohua Wang Copyright © 2017 Yuming Zhao et al. All rights reserved. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors Mon, 29 May 2017 00:00:00 +0000 Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with (cross-validated correlation coefficient) and (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR. Manman Zhao, Lin Wang, Linfeng Zheng, Mengying Zhang, Chun Qiu, Yuhui Zhang, Dongshu Du, and Bing Niu Copyright © 2017 Manman Zhao et al. All rights reserved. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks Sun, 28 May 2017 06:34:07 +0000 Many structural variations (SVs) detection methods have been proposed due to the popularization of next-generation sequencing (NGS). These SV calling methods use different SV-property-dependent features; however, they all suffer from poor accuracy when running on low coverage sequences. The union of results from these tools achieves fairly high sensitivity but still produces low accuracy on low coverage sequence data. That is, these methods contain many false positives. In this paper, we present CNNdel, an approach for calling deletions from paired-end reads. CNNdel gathers SV candidates reported by multiple tools and then extracts features from aligned BAM files at the positions of candidates. With labeled feature-expressed candidates as a training set, CNNdel trains convolutional neural networks (CNNs) to distinguish true unlabeled candidates from false ones. Results show that CNNdel works well with NGS reads from 26 low coverage genomes of the 1000 Genomes Project. The paper demonstrates that convolutional neural networks can automatically assign the priority of SV features and reduce the false positives efficaciously. Jing Wang, Cheng Ling, and Jingyang Gao Copyright © 2017 Jing Wang et al. All rights reserved. A Cancer Gene Selection Algorithm Based on the K-S Test and CFS Mon, 08 May 2017 08:37:33 +0000 Background. To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. Results. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR), and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. Conclusions. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms. Qiang Su, Yina Wang, Xiaobing Jiang, Fuxue Chen, and Wen-cong Lu Copyright © 2017 Qiang Su et al. All rights reserved. Identification of Transcriptional Modules and Key Genes in Chickens Infected with Salmonella enterica Serovar Pullorum Using Integrated Coexpression Analyses Wed, 26 Apr 2017 00:00:00 +0000 Salmonella enterica Pullorum is one of the leading causes of mortality in poultry. Understanding the molecular response in chickens in response to the infection by S. enterica is important in revealing the mechanisms of pathogenesis and disease progress. There have been studies on identifying genes associated with Salmonella infection by differential expression analysis, but the relationships among regulated genes have not been investigated. In this study, we employed weighted gene coexpression network analysis (WGCNA) and differential coexpression analysis (DCEA) to identify coexpression modules by exploring microarray data derived from chicken splenic tissues in response to the S. enterica infection. A total of 19 modules from 13,538 genes were associated with the Jak-STAT signaling pathway, the extracellular matrix, cytoskeleton organization, the regulation of the actin cytoskeleton, G-protein coupled receptor activity, Toll-like receptor signaling pathways, and immune system processes; among them, 14 differentially coexpressed modules (DCMs) and 2,856 differentially coexpressed genes (DCGs) were identified. The global expression of module genes between infected and uninfected chickens showed slight differences but considerable changes for global coexpression. Furthermore, DCGs were consistently linked to the hubs of the modules. These results will help prioritize candidate genes for future studies of Salmonella infection. Bao-Hong Liu and Jian-Ping Cai Copyright © 2017 Bao-Hong Liu and Jian-Ping Cai. All rights reserved. A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model Tue, 11 Apr 2017 00:00:00 +0000 The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them. Haitao Guo and Hongwei Huo Copyright © 2017 Haitao Guo and Hongwei Huo. All rights reserved. Joint -Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction Sun, 02 Apr 2017 08:39:45 +0000 Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting constraint ( gLPCA) on error function for feature (gene) extraction. The error function based on -norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others. Chun-Mei Feng, Ying-Lian Gao, Jin-Xing Liu, Juan Wang, Dong-Qin Wang, and Chang-Gang Wen Copyright © 2017 Chun-Mei Feng et al. All rights reserved. Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation Thu, 30 Mar 2017 06:47:17 +0000 Aiming at the problem of gene expression profile’s high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual graph manifold regularized constraint, the NNDGLLRR can keep the internal spatial structure of the original data effectively and improve the final clustering accuracy while segmenting the subspace. The introduction of nonnegative constraints makes the computation with some sparsity, which enhances the robustness of the algorithm. Different from Lat-LRR, a new solution model is adopted to simplify the computational complexity. The experimental results show that the proposed algorithm has good feature extraction performance for the heavy redundancy and noise gene expression profile, which, compared with LRR and Lat-LRR, can achieve better clustering accuracy. Guoliang Yang and Zhengwei Hu Copyright © 2017 Guoliang Yang and Zhengwei Hu. All rights reserved. Dissect the Dynamic Molecular Circuits of Cell Cycle Control through Network Evolution Model Thu, 30 Mar 2017 00:00:00 +0000 The molecular circuits of cell cycle control serve as a key hub to integrate from endogenous and environmental signals into a robust biological decision driving cell growth and division. Dysfunctional cell cycle control is highlighted in a wide spectrum of human cancers. More importantly the mainstay anticancer treatment such as radiation therapy and chemotherapy targets the hallmark of uncontrolled cell proliferation in cancer cells by causing DNA damage, cell cycle arrest, and cell death. Given the functional importance of cell cycle control, the regulatory mechanisms that drive the cell division have been extensively investigated in a huge number of studies by conventional single-gene approaches. However the complexity of cell cycle control renders a significant barrier to understand its function at a network level. In this study, we used mathematical modeling through modern graph theory and differential equation systems. We believe our network evolution model can help us understand the dynamic cell cycle control in tumor evolution and optimizing dosing schedules for radiation therapy and chemotherapy targeting cell cycle. Yang Peng, Paul Scott, Ruikang Tao, Hua Wang, Yan Wu, and Guang Peng Copyright © 2017 Yang Peng et al. All rights reserved. Translocation of a Polymer through a Crowded Channel under Electrical Force Sun, 26 Mar 2017 07:56:44 +0000 The translocation of a polymer chain through a crowded cylindrical channel is studied using the Langevin dynamics simulations. The influences of the field strength , the chain length , and the crowding extent on the translocation time are evaluated, respectively. Scaling relation is observed. With the crowding extent increasing, the scaling exponent becomes large. It is found that, for noncrowded channel, translocation probability drops when the field strength becomes large. However, for high-crowded channel, it is the opposite. Moreover, the translocation time and the average translocation time for all segments both have exponential growth with the crowding extent. The investigation of shape factor shows maximum value with increasing of the number of segments outside . At last, the number of segments inside channel in the process of translocation is calculated and a peak is observed. All the information from the study may benefit protein translocation. Tingting Sun, Yunxin Gen, Hujun Xie, Zhouting Jiang, and Zhiyong Yang Copyright © 2017 Tingting Sun et al. All rights reserved. Novel Computational Approaches and Applications in Cancer Research Tue, 21 Mar 2017 08:18:30 +0000 Min Zhang, Lin Hua, Weiwei Zhai, and Yichuan Zhao Copyright © 2017 Min Zhang et al. All rights reserved. Curcumin Analogue CA15 Exhibits Anticancer Effects on HEp-2 Cells via Targeting NF-κB Mon, 20 Mar 2017 08:33:09 +0000 Laryngeal carcinoma remains one of the most common malignancies, and curcumin has been proven to be effective against head and neck cancers in vitro. However, it has not yet been applied in clinical settings due to its low stability. In the current study, we synthesized 34 monocarbonyl analogues of curcumin with stable structures. CA15, which exhibited a stronger inhibited effect on laryngeal cancer cells HEp-2 but a lower toxicity on hepatic cells HL-7702 in MTT assay, was selected for further analysis. The effects of CA15 on cell viability, proliferation, migration, apoptosis, and NF-κB activation were measured using MTT, Transwell migration, flow cytometry, Western blot, and immunofluorescence assays in HEp-2 cells. An NF-κB inhibitor, BMS-345541, as well as curcumin was also tested. Results showed that CA15 induced decreased toxicity towards HL-7702 cells compared to curcumin and BMS-345541. However, similar to BMS-345541 and curcumin, CA15 not only significantly inhibited proliferation and migration and induced caspase-3-dependent apoptosis but also attenuated TNF-α-induced NF-κB activation in HEp-2 cells. These results demonstrated that curcumin analogue CA15 exhibited anticancer effects on laryngeal cancer cells via targeting of NF-κB. Jian Chen, Linlin Zhang, Yilai Shu, Liping Chen, Min Zhu, Song Yao, Jiabing Wang, Jianzhang Wu, Guang Liang, Haitao Wu, and Wulan Li Copyright © 2017 Jian Chen et al. All rights reserved. Computational Identification and Characterization of a Promiscuous T-Cell Epitope on the Extracellular Protein 85B of Mycobacterium spp. for Peptide-Based Subunit Vaccine Design Thu, 16 Mar 2017 00:00:00 +0000 Tuberculosis (TB) is a reemerging disease that remains as a leading cause of morbidity and mortality in humans. To identify and characterize a T-cell epitope suitable for vaccine design, we have utilized the Vaxign server to assess all antigenic proteins of Mycobacterium spp. recorded to date in the Protegen database. We found that the extracellular protein 85B displayed the most robust antigenicity among the proteins identified. Computational tools for identifying T-cell epitopes predicted an epitope, 181-QQFIYAGSLSALLDP-195, that could bind to at least 13 major histocompatibility complexes, revealing the promiscuous nature of the epitope. Molecular docking simulation demonstrated that the epitope could bind to the binding groove of MHC II and MHC I molecules by several hydrogen bonds. Molecular docking analysis further revealed that the epitope had a distinctive binding pattern to all DRB1 and A and B series of MHC molecules and presented almost no polymorphism in its binding site. Moreover, using “Allele Frequency Database,” we checked the frequency of HLA alleles in the worldwide population and found a higher frequency of both class I and II HLA alleles in individuals living in TB-endemic regions. Our results indicate that the identified peptide might be a universal candidate to produce an efficient epitope-based vaccine for TB. Md. Saddam Hossain, Abul Kalam Azad, Parveen Afroz Chowdhury, and Mamoru Wakayama Copyright © 2017 Md. Saddam Hossain et al. All rights reserved. Frequency Specific Effects of ApoE ε4 Allele on Resting-State Networks in Nondemented Elders Wed, 15 Mar 2017 00:00:00 +0000 We applied resting-state functional magnetic resonance imaging (fMRI) to examine the Apolipoprotein E (ApoE) ε4 allele effects on functional connectivity of the default mode network (DMN) and the salience network (SN). Considering the frequency specific effects of functional connectivity, we decomposed the brain network time courses into two bands: 0.01–0.027 Hz and 0.027–0.08 Hz. All scans were acquired by the Alzheimer’s Disease Neuroscience Initiative (ADNI). Thirty-two nondemented subjects were divided into two groups based on the presence () or absence () of the ApoE ε4 allele. We explored the frequency specific effects of ApoE ε4 allele on the default mode network (DMN) and the salience network (SN) functional connectivity. Compared to ε4 noncarriers, the DMN functional connectivity of ε4 carriers was significantly decreased while the SN functional connectivity of ε4 carriers was significantly increased. Many functional connectivities showed significant differences at the lower frequency band of 0.01–0.027 Hz or the higher frequency band of 0.027–0.08 Hz instead of the typical range of 0.01–0.08 Hz. The results indicated a frequency dependent effect of resting-state signals when investigating RSNs functional connectivity. Ying Liang, Zhenzhen Li, Jing Wei, Chunlin Li, Xu Zhang, and Alzheimer’s Disease Neuroimaging Initiative Copyright © 2017 Ying Liang et al. All rights reserved. Mathematical and Computational Modeling in Complex Biological Systems Mon, 13 Mar 2017 00:00:00 +0000 The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology. Zhiwei Ji, Ke Yan, Wenyang Li, Haigen Hu, and Xiaoliang Zhu Copyright © 2017 Zhiwei Ji et al. All rights reserved. COPAR: A ChIP-Seq Optimal Peak Analyzer Sun, 05 Mar 2017 08:19:03 +0000 Sequencing data quality and peak alignment efficiency of ChIP-sequencing profiles are directly related to the reliability and reproducibility of NGS experiments. Till now, there is no tool specifically designed for optimal peak alignment estimation and quality-related genomic feature extraction for ChIP-sequencing profiles. We developed open-sourced COPAR, a user-friendly package, to statistically investigate, quantify, and visualize the optimal peak alignment and inherent genomic features using ChIP-seq data from NGS experiments. It provides a versatile perspective for biologists to perform quality-check for high-throughput experiments and optimize their experiment design. The package COPAR can process mapped ChIP-seq read file in BED format and output statistically sound results for multiple high-throughput experiments. Together with three public ChIP-seq data sets verified with the developed package, we have deposited COPAR on GitHub under a GNU GPL license. Binhua Tang, Xihan Wang, and Victor X. Jin Copyright © 2017 Binhua Tang et al. All rights reserved. Scalable Data Mining Algorithms in Computational Biology and Biomedicine Tue, 28 Feb 2017 13:52:45 +0000 Quan Zou, Dariusz Mrozek, Qin Ma, and Yungang Xu Copyright © 2017 Quan Zou et al. All rights reserved. Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship Tue, 28 Feb 2017 00:00:00 +0000 The current use of a single chemical component as the representative quality control marker of herbal food supplement is inadequate. In this CD80-Quantitative-Pattern-Activity-Relationship (QPAR) study, we built a bioactivity predictive model that can be applicable for complex mixtures. Through integrating the chemical fingerprinting profiles of the immunomodulating herb Radix Astragali (RA) extracts, and their related biological data of immunological marker CD80 expression on dendritic cells, a chemometric model using the Elastic Net Partial Least Square (EN-PLS) algorithm was established. The EN-PLS algorithm increased the biological predictive capability with lower value of RMSEP (11.66) and higher values of (0.55) when compared to the standard PLS model. This CD80-QPAR platform provides a useful predictive model for unknown RA extract’s bioactivities using the chemical fingerprint inputs. Furthermore, this bioactivity prediction platform facilitates identification of key bioactivity-related chemical components within complex mixtures for future drug discovery and understanding of the batch-to-batch consistency for quality clinical trials. Michelle Chun-har Ng, Tsui-yan Lau, Kei Fan, Qing-song Xu, Josiah Poon, Simon K. Poon, Mary K. Lam, Foo-tim Chau, and Daniel Man-Yuen Sze Copyright © 2017 Michelle Chun-har Ng et al. All rights reserved. Different Densities of Na-Ca Exchange Current in T-Tubular and Surface Membranes and Their Impact on Cellular Activity in a Model of Rat Ventricular Cardiomyocyte Wed, 22 Feb 2017 10:22:03 +0000 The ratio of densities of Na-Ca exchanger current () in the t-tubular and surface membranes (-ratio) computed from the values of and membrane capacitances () measured in adult rat ventricular cardiomyocytes before and after detubulation ranges between 1.7 and 25 (potentially even 40). Variations of action potential waveform and of calcium turnover within this span of the -ratio were simulated employing previously developed model of rat ventricular cell incorporating separate description of ion transport systems in the t-tubular and surface membranes. The increase of -ratio from 1.7 to 25 caused a prolongation of APD (duration of action potential at 90% repolarisation) by 12, 9, and 6% and an increase of peak intracellular Ca2+ transient by 45, 19, and 6% at 0.1, 1, and 5 Hz, respectively. The prolonged APD resulted from the increase of due to the exposure of a larger fraction of Na-Ca exchangers to higher Ca2+ transients under the t-tubular membrane. The accompanying rise of Ca2+ transient was a consequence of a higher Ca2+ load in sarcoplasmic reticulum induced by the increased Ca2+ cycling between the surface and t-tubular membranes. However, the reason for large differences in the -ratio assessed from measurements in adult rat cardiomyocytes remains to be explained. M. Pásek, J. Šimurda, and G. Christé Copyright © 2017 M. Pásek et al. All rights reserved. MicroRNA Mediating Networks in Granulosa Cells Associated with Ovarian Follicular Development Sun, 19 Feb 2017 00:00:00 +0000 Ovaries, which provide a place for follicular development and oocyte maturation, are important organs in female mammals. Follicular development is complicated physiological progress mediated by various regulatory factors including microRNAs (miRNAs). To demonstrate the role of miRNAs in follicular development, this study analyzed the expression patterns of miRNAs in granulosa cells through investigating three previous datasets generated by Illumina miRNA deep sequencing. Furthermore, via bioinformatic analyses, we dissected the associated functional networks of the observed significant miRNAs, in terms of interacting with signal pathways and transcription factors. During the growth and selection of dominant follicles, 15 dysregulated miRNAs and 139 associated pathways were screened out. In comparison of different styles of follicles, 7 commonly abundant miRNAs and 195 pathways, as well as 10 differentially expressed miRNAs and 117 pathways in dominant follicles in comparison with subordinate follicles, were collected. Furthermore, SMAD2 was identified as a hub factor in regulating follicular development. The regulation of miR-26a/b on smad2 messenger RNA has been further testified by real time PCR. In conclusion, we established functional networks which play critical roles in follicular development including pivotal miRNAs, pathways, and transcription factors, which contributed to the further investigation about miRNAs associated with mammalian follicular development. Baoyun Zhang, Long Chen, Guangde Feng, Wei Xiang, Ke Zhang, Mingxing Chu, and Pingqing Wang Copyright © 2017 Baoyun Zhang et al. All rights reserved. Network Proteomics: From Protein Structure to Protein-Protein Interaction Thu, 16 Feb 2017 00:00:00 +0000 Guang Hu, Luisa Di Paola, Filippo Pullara, Zhongjie Liang, and Intawat Nookaew Copyright © 2017 Guang Hu et al. All rights reserved. Combined Ligand/Structure-Based Virtual Screening and Molecular Dynamics Simulations of Steroidal Androgen Receptor Antagonists Wed, 15 Feb 2017 00:00:00 +0000 The antiandrogens, such as bicalutamide, targeting the androgen receptor (AR), are the main endocrine therapies for prostate cancer (PCa). But as drug resistance to antiandrogens emerges in advanced PCa, there presents a high medical need for exploitation of novel AR antagonists. In this work, the relationships between the molecular structures and antiandrogenic activities of a series of 7α-substituted dihydrotestosterone derivatives were investigated. The proposed MLR model obtained high predictive ability. The thoroughly validated QSAR model was used to virtually screen new dihydrotestosterones derivatives taken from PubChem, resulting in the finding of novel compounds CID_70128824, CID_70127147, and CID_70126881, whose in silico bioactivities are much higher than the published best one, even higher than bicalutamide. In addition, molecular docking, molecular dynamics (MD) simulations, and MM/GBSA have been employed to analyze and compare the binding modes between the novel compounds and AR. Through the analysis of the binding free energy and residue energy decomposition, we concluded that the newly discovered chemicals can in silico bind to AR with similar position and mechanism to the reported active compound and the van der Waals interaction is the main driving force during the binding process. Yuwei Wang, Rui Han, Huimin Zhang, Hongli Liu, Jiazhong Li, Huanxiang Liu, and Paola Gramatica Copyright © 2017 Yuwei Wang et al. All rights reserved. Metabolomic Biomarker Identification in Presence of Outliers and Missing Values Tue, 14 Feb 2017 06:58:24 +0000 Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, -test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease. Nishith Kumar, Md. Aminul Hoque, Md. Shahjaman, S. M. Shahinul Islam, and Md. Nurul Haque Mollah Copyright © 2017 Nishith Kumar et al. All rights reserved. Identification of Candidate Genes Related to Inflammatory Bowel Disease Using Minimum Redundancy Maximum Relevance, Incremental Feature Selection, and the Shortest-Path Approach Tue, 14 Feb 2017 00:00:00 +0000 Identification of disease genes is a hot topic in biomedicine and genomics. However, it is a challenging problem because of the complexity of diseases. Inflammatory bowel disease (IBD) is an idiopathic disease caused by a dysregulated immune response to host intestinal microflora. It has been proven to be associated with the development of intestinal malignancies. Although the specific pathological characteristics and genetic background of IBD have been partially revealed, it is still an overdetermined disease and the blueprint of all genetic variants still needs to be improved. In this study, a novel computational method was built to identify genes related to IBD. Samples from two subtypes of IBD (ulcerative colitis and Crohn’s disease) and normal samples were employed. By analyzing the gene expression profiles of these samples using minimum redundancy maximum relevance and incremental feature selection, 21 genes were obtained that could effectively distinguish samples from the two subtypes of IBD and the normal samples. Then, the shortest-path approach was used to search for an additional 20 genes in a large network constructed using protein-protein interactions based on the above-mentioned 21 genes. Analyses of the 41 genes obtained indicate that they are closely associated with this disease. Fei Yuan, Yu-Hang Zhang, Xiang-Yin Kong, and Yu-Dong Cai Copyright © 2017 Fei Yuan et al. All rights reserved. Numerical Method for the Design of Healing Chamber in Additive-Manufactured Dental Implants Sun, 12 Feb 2017 00:00:00 +0000 The inclusion of a healing chamber in dental implants has been shown to promote biological healing. In this paper, a novel numerical approach to the design of the healing chamber for additive-manufactured dental implants is proposed. This study developed an algorithm for the modeling of bone growth and employed finite element method in ANSYS to facilitate the design of healing chambers with a highly complex configuration. The model was then applied to the design of dental implants for insertion into the posterior maxillary bones. Two types of ITI® solid cylindrical screwed implant with extra rectangular-shaped healing chamber as an initial design are adopted, with which to evaluate the proposed system. This resulted in several configurations for the healing chamber, which were then evaluated based on the corresponding volume fraction of healthy surrounding bone. The best of these implants resulted in a healing chamber surrounded by around 9.2% more healthy bone than that obtained from the original design. The optimal design increased the contact area between the bone and implant by around 52.9%, which is expected to have a significant effect on osseointegration. The proposed approach is highly efficient which typically completes the optimization of each implant within 3–5 days on an ordinary personal computer. It is also sufficiently general to permit extension to various loading conditions. Hsiao-Chien Lee, Pei-I Tsai, Chih-Chieh Huang, San-Yuan Chen, Chuen-Guang Chao, and Nien-Ti Tsou Copyright © 2017 Hsiao-Chien Lee et al. All rights reserved. Cancer-Related Triplets of mRNA-lncRNA-miRNA Revealed by Integrative Network in Uterine Corpus Endometrial Carcinoma Wed, 08 Feb 2017 00:00:00 +0000 The regulation of transcriptome expression level is a complex process involving multiple-level interactions among molecules such as protein coding RNA (mRNA), long noncoding RNA (lncRNA), and microRNA (miRNA), which are essential for the transcriptome stability and maintenance and regulation of body homeostasis. The availability of multilevel expression data enables a comprehensive view of the regulatory network. In this study, we analyzed the coding and noncoding gene expression profiles of 301 patients with uterine corpus endometrial carcinoma (UCEC). A new method was proposed to construct a genome-wide integrative network based on variance inflation factor (VIF) regression method. The cross-regulation relations of mRNA, lncRNA, and miRNA were then selected based on clique-searching algorithm from the network, when any two molecules of the three were shown as interacting according to the integrative network. Such relation, which we call the mRNA-lncRNA-miRNA triplet, demonstrated the complexity in transcriptome regulation process. Finally, six UCEC-related triplets were selected in which the mRNA participates in endometrial carcinoma pathway, such as CDH1 and TP53. The multi-type RNAs are proved to be cross-regulated as to each of the six triplets according to literature. All the triplets demonstrated the association with the initiation and progression of UCEC. Our method provides a comprehensive strategy for the investigation of transcriptome regulation mechanism. Chenglin Liu, Yu-Hang Zhang, Qinfang Deng, Yixue Li, Tao Huang, Songwen Zhou, and Yu-Dong Cai Copyright © 2017 Chenglin Liu et al. All rights reserved. Objective Ventricle Segmentation in Brain CT with Ischemic Stroke Based on Anatomical Knowledge Tue, 07 Feb 2017 00:00:00 +0000 Ventricle segmentation is a challenging technique for the development of detection system of ischemic stroke in computed tomography (CT), as ischemic stroke regions are adjacent to the brain ventricle with similar intensity. To address this problem, we developed an objective segmentation system of brain ventricle in CT. The intensity distribution of the ventricle was estimated based on clustering technique, connectivity, and domain knowledge, and the initial ventricle segmentation results were then obtained. To exclude the stroke regions from initial segmentation, a combined segmentation strategy was proposed, which is composed of three different schemes: (1) the largest three-dimensional (3D) connected component was considered as the ventricular region; (2) the big stroke areas were removed by the image difference methods based on searching optimal threshold values; (3) the small stroke regions were excluded by the adaptive template algorithm. The proposed method was evaluated on 50 cases of patients with ischemic stroke. The mean Dice, sensitivity, specificity, and root mean squared error were 0.9447, 0.969, 0.998, and 0.219 mm, respectively. This system can offer a desirable performance. Therefore, the proposed system is expected to bring insights into clinic research and the development of detection system of ischemic stroke in CT. Xiaohua Qian, Yuan Lin, Yue Zhao, Xinyan Yue, Bingheng Lu, and Jing Wang Copyright © 2017 Xiaohua Qian et al. All rights reserved. Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm Wed, 01 Feb 2017 06:11:33 +0000 As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients’ personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases. Wei Guo, Dong-Mei Shang, Jing-Hui Cao, Kaiyan Feng, Yi-Chun He, Yang Jiang, ShaoPeng Wang, and Yu-Fei Gao Copyright © 2017 Wei Guo et al. All rights reserved. Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data Mon, 23 Jan 2017 00:00:00 +0000 Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding. Bing Jiang, Shuwen Li, Zhi Jiang, and Ping Shao Copyright © 2017 Bing Jiang et al. All rights reserved. Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case Sun, 22 Jan 2017 00:00:00 +0000 The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology. Guang Hu, Luisa Di Paola, Zhongjie Liang, and Alessandro Giuliani Copyright © 2017 Guang Hu et al. All rights reserved. Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics Mon, 16 Jan 2017 07:15:09 +0000 Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma. Hao Long, Chaofeng Liang, Xi’an Zhang, Luxiong Fang, Gang Wang, Songtao Qi, Haizhong Huo, and Ye Song Copyright © 2017 Hao Long et al. All rights reserved. Translational Molecular Imaging Computing: Advances in Theories and Applications Tue, 20 Dec 2016 11:13:19 +0000 Jinchao Feng, Wenxiang Cong, Kuangyu Shi, Shouping Zhu, and Jun Zhang Copyright © 2016 Jinchao Feng et al. All rights reserved. Corrigendum to “Transmission Model of Hepatitis B Virus with the Migration Effect” Tue, 20 Dec 2016 08:41:30 +0000 Muhammad Altaf Khan, Saeed Islam, Muhammad Arif, and Zahoor ul Haq Copyright © 2016 Muhammad Altaf Khan et al. All rights reserved. Current Computational Models for Prediction of the Varied Interactions Related to Noncoding RNAs Thu, 15 Dec 2016 13:02:45 +0000 Xing Chen, Huiming Peng, and Zheng Yin Copyright © 2016 Xing Chen et al. All rights reserved. The Analysis of Plantar Pressure Data Based on Multimodel Method in Patients with Anterior Cruciate Ligament Deficiency during Walking Tue, 06 Dec 2016 12:02:56 +0000 The movement information of the human body can be recorded in the plantar pressure data, and the analysis of plantar pressure data can be used to judge whether the human body motion function is normal or not. A two-meter footscan® system was used to collect the plantar pressure data, and the kinetic and dynamic gait characteristics were extracted. According to the different description of gait characteristics, a set of models was established according to various people to present the movement of lower limbs. By the introduction of algorithm in machine learning, the FCM clustering algorithm is used to cluster the sample set and create a set of models, and then the SVM algorithm was used to identify the new samples, so as to complete the normal and abnormal motion function identification. The multimodel presented in this paper was carried out into the analysis of the anterior cruciate ligament deficiency. This method demonstrated being effective and can provide auxiliary analysis for clinical diagnosis. Xiaoli Li, Hongshi Huang, Jie Wang, Yuanyuan Yu, and Yingfang Ao Copyright © 2016 Xiaoli Li et al. All rights reserved. Fast and Robust Reconstruction for Fluorescence Molecular Tomography via Regularization Tue, 06 Dec 2016 11:42:24 +0000 Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of and norms. To solve the nonconvex minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for minimization outperforms the representative algorithms for , , , and when the system matrix is highly coherent. Haibo Zhang, Guohua Geng, Xiaodong Wang, Xuan Qu, Yuqing Hou, and Xiaowei He Copyright © 2016 Haibo Zhang et al. All rights reserved. Novel Biomarker MicroRNAs for Subtyping of Acute Coronary Syndrome: A Bioinformatics Approach Thu, 01 Dec 2016 14:14:42 +0000 Acute coronary syndrome (ACS) is a life-threatening disease that affects more than half a million people in United States. We currently lack molecular biomarkers to distinguish the unstable angina (UA) and acute myocardial infarction (AMI), which are the two subtypes of ACS. MicroRNAs play significant roles in biological processes and serve as good candidates for biomarkers. In this work, we collected microRNA datasets from the Gene Expression Omnibus database and identified specific microRNAs in different subtypes and universal microRNAs in all subtypes based on our novel network-based bioinformatics approach. These microRNAs were studied for ACS association by pathway enrichment analysis of their target genes. AMI and UA were associated with 27 and 26 microRNAs, respectively, nine of them were detected for both AMI and UA, and five from each subtype had been reported previously. The remaining 22 and 21 microRNAs are novel microRNA biomarkers for AMI and UA, respectively. The findings are then supported by pathway enrichment analysis of the targets of these microRNAs. These novel microRNAs deserve further validation and will be helpful for personalized ACS diagnosis. Yujie Zhu, Yuxin Lin, Wenying Yan, Zhandong Sun, Zhi Jiang, Bairong Shen, Xiaoqian Jiang, and Jingjing Shi Copyright © 2016 Yujie Zhu et al. All rights reserved. Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination Tue, 29 Nov 2016 10:04:50 +0000 Long noncoding RNA (lncRNA) is a kind of noncoding RNA with length more than 200 nucleotides, which aroused interest of people in recent years. Lots of studies have confirmed that human genome contains many thousands of lncRNAs which exert great influence over some critical regulators of cellular process. With the advent of high-throughput sequencing technologies, a great quantity of sequences is waiting for exploitation. Thus, many programs are developed to distinguish differences between coding and long noncoding transcripts. Different programs are generally designed to be utilised under different circumstances and it is sensible and practical to select an appropriate method according to a certain situation. In this review, several popular methods and their advantages, disadvantages, and application scopes are summarised to assist people in employing a suitable method and obtaining a more reliable result. Siyu Han, Yanchun Liang, Ying Li, and Wei Du Copyright © 2016 Siyu Han et al. All rights reserved. Depth Attenuation Degree Based Visualization for Cardiac Ischemic Electrophysiological Feature Exploration Sun, 27 Nov 2016 11:48:50 +0000 Although heart researches and acquirement of clinical and experimental data are progressively open to public use, cardiac biophysical functions are still not well understood. Due to the complex and fine structures of the heart, cardiac electrophysiological features of interest may be occluded when there is a necessity to demonstrate cardiac electrophysiological behaviors. To investigate cardiac abnormal electrophysiological features under the pathological condition, in this paper, we implement a human cardiac ischemic model and acquire the electrophysiological data of excitation propagation. A visualization framework is then proposed which integrates a novel depth weighted optic attenuation model into the pathological electrophysiological model. The hidden feature of interest in pathological tissue can be revealed from sophisticated overlapping biophysical information. Experiment results verify the effectiveness of the proposed method for intuitively exploring and inspecting cardiac electrophysiological activities, which is fundamental in analyzing and explaining biophysical mechanisms of cardiac functions for doctors and medical staff. Fei Yang, Lei Zhang, Weigang Lu, Lei Liu, Yue Zhang, Wangmeng Zuo, Kuanquan Wang, and Henggui Zhang Copyright © 2016 Fei Yang et al. All rights reserved. Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles Thu, 24 Nov 2016 12:45:25 +0000 Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, -nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data. Liying Yang, Zhimin Liu, Xiguo Yuan, Jianhua Wei, and Junying Zhang Copyright © 2016 Liying Yang et al. All rights reserved. A Sparsity-Constrained Preconditioned Kaczmarz Reconstruction Method for Fluorescence Molecular Tomography Thu, 24 Nov 2016 06:16:05 +0000 Fluorescence molecular tomography (FMT) is an imaging technique that can localize and quantify fluorescent markers to resolve biological processes at molecular and cellular levels. Owing to a limited number of measurements and a large number of unknowns as well as the diffusive transport of photons in biological tissues, the inverse problem in FMT is usually highly ill-posed. In this work, a sparsity-constrained preconditioned Kaczmarz (SCP-Kaczmarz) method is proposed to reconstruct the fluorescent target for FMT. The SCP-Kaczmarz method uses the preconditioning strategy to minimize the correlation between the rows of the forward matrix and constrains the Kaczmarz iteration results to be sparse. Numerical simulation and phantom and in vivo experiments were performed to test the efficiency of the proposed method. The results demonstrate that both the convergence and accuracy of the proposed method are improved compared with the classical memory-efficient low-cost Kaczmarz method. Duofan Chen, Jimin Liang, Yao Li, and Guanghui Qiu Copyright © 2016 Duofan Chen et al. All rights reserved. Effect of Dynamic Interaction between microRNA and Transcription Factor on Gene Expression Thu, 10 Nov 2016 07:50:14 +0000 MicroRNAs (miRNAs) are endogenous noncoding RNAs which participate in diverse biological processes in animals and plants. They are known to join together with transcription factors and downstream gene, forming a complex and highly interconnected regulatory network. To recognize a few overrepresented motifs which are expected to perform important elementary regulatory functions, we constructed a computational model of miRNA-mediated feedforward loops (FFLs) in which a transcription factor (TF) regulates miRNA and targets gene. Based on the different dynamic interactions between miRNA and TF on gene expression, four possible structural topologies of FFLs with two gate functions (AND gate and OR gate) are introduced. We studied the dynamic behaviors of these different motifs. Furthermore, the relationship between the response time and maximal activation velocity of miRNA was investigated. We found that the curve of response time shows nonmonotonic behavior in Co1 loop with OR gate. This may help us to infer the mechanism of miRNA binding to the promoter region. At last we investigated the influence of important parameters on the dynamic response of system. We identified that the stationary levels of target gene in all loops were insensitive to the initial value of miRNA. Qi Zhao, Hongsheng Liu, Chenggui Yao, Jianwei Shuai, and Xiaoqiang Sun Copyright © 2016 Qi Zhao et al. All rights reserved. Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer Wed, 09 Nov 2016 13:07:49 +0000 Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer. Hang Yin, ShaoPeng Wang, Yu-Hang Zhang, Yu-Dong Cai, and Hailin Liu Copyright © 2016 Hang Yin et al. All rights reserved. Biomolecular Network-Based Synergistic Drug Combination Discovery Mon, 07 Nov 2016 11:27:08 +0000 Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing. Xiangyi Li, Guangrong Qin, Qingmin Yang, Lanming Chen, and Lu Xie Copyright © 2016 Xiangyi Li et al. All rights reserved. Functional Region Annotation of Liver CT Image Based on Vascular Tree Mon, 07 Nov 2016 09:38:15 +0000 Anatomical analysis of liver region is critical in diagnosis and treatment of liver diseases. The reports of liver region annotation are helpful for doctors to precisely evaluate liver system. One of the challenging issues is to annotate the functional regions of liver through analyzing Computed Tomography (CT) images. In this paper, we propose a vessel-tree-based liver annotation method for CT images. The first step of the proposed annotation method is to extract the liver region including vessels and tumors from the CT scans. And then a 3-dimensional thinning algorithm is applied to obtain the spatial skeleton and geometric structure of liver vessels. With the vessel skeleton, the topology of portal veins is further formulated by a directed acyclic graph with geometrical attributes. Finally, based on the topological graph, a hierarchical vascular tree is constructed to divide the liver into eight segments according to Couinaud classification theory and thereby annotate the functional regions. Abundant experimental results demonstrate that the proposed method is effective for precise liver annotation and helpful to support liver disease diagnosis. Yufei Chen, Xiaodong Yue, Caiming Zhong, and Gang Wang Copyright © 2016 Yufei Chen et al. All rights reserved. The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure Sun, 06 Nov 2016 09:30:54 +0000 Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and -nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods. Fausto Lucena, Allan Kardec Barros, and Noboru Ohnishi Copyright © 2016 Fausto Lucena et al. All rights reserved. Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes Wed, 02 Nov 2016 09:16:19 +0000 Residue fluctuations in protein structures have been shown to be highly associated with various protein functions. Gaussian network model (GNM), a simple representative coarse-grained model, was widely adopted to reveal function-related protein dynamics. We directly utilized the high frequency modes generated by GNM and further performed Gaussian Naive Bayes (GNB) to identify hot spot residues. Two coding schemes about the feature vectors were implemented with varying distance cutoffs for GNM and sliding window sizes for GNB based on tenfold cross validations: one by using only a single high mode and the other by combining multiple modes with the highest frequency. Our proposed methods outperformed the previous work that did not directly utilize the high frequency modes generated by GNM, with regard to overall performance evaluated using measure. Moreover, we found that inclusion of more high frequency modes for a GNB classifier can significantly improve the sensitivity. The present study provided additional valuable insights into the relation between the hot spots and the residue fluctuations. Hua Zhang, Tao Jiang, and Guogen Shan Copyright © 2016 Hua Zhang et al. All rights reserved. Identification of Novel Inhibitors against Coactivator Associated Arginine Methyltransferase 1 Based on Virtual Screening and Biological Assays Thu, 27 Oct 2016 10:08:58 +0000 Overexpression of coactivator associated arginine methyltransferase 1 (CARM1), a protein arginine N-methyltransferase (PRMT) family enzyme, is associated with various diseases including cancers. Consequently, the development of small-molecule inhibitors targeting PRMTs has significant value for both research and therapeutic purposes. In this study, together with structure-based virtual screening with biochemical assays, two compounds DC_C11 and DC_C66 were identified as novel inhibitors of CARM1. Cellular studies revealed that the two inhibitors are cell membrane permeable and effectively blocked proliferation of cancer cells including HELA, K562, and MCF7. We further predicted the binding mode of these inhibitors through molecular docking analysis, which indicated that the inhibitors competitively occupied the binding site of the substrate and destroyed the protein-protein interactions between CARM1 and its substrates. Overall, this study has shed light on the development of small-molecule CARM1 inhibitors with novel scaffolds. Fei Ye, Weiyao Zhang, Wenchao Lu, Yiqian Xie, Hao Jiang, Jia Jin, and Cheng Luo Copyright © 2016 Fei Ye et al. All rights reserved. Cone Beam X-Ray Luminescence Tomography Imaging Based on KA-FEM Method for Small Animals Thu, 27 Oct 2016 06:37:10 +0000 Cone beam X-ray luminescence tomography can realize fast X-ray luminescence tomography imaging with relatively low scanning time compared with narrow beam X-ray luminescence tomography. However, cone beam X-ray luminescence tomography suffers from an ill-posed reconstruction problem. First, the feasibility of experiments with different penetration and multispectra in small animal has been tested using nanophosphor material. Then, the hybrid reconstruction algorithm with KA-FEM method has been applied in cone beam X-ray luminescence tomography for small animals to overcome the ill-posed reconstruction problem, whose advantage and property have been demonstrated in fluorescence tomography imaging. The in vivo mouse experiment proved the feasibility of the proposed method. Dongmei Chen, Fanzhen Meng, Fengjun Zhao, and Cao Xu Copyright © 2016 Dongmei Chen et al. All rights reserved. Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images Wed, 26 Oct 2016 13:26:12 +0000 The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of them -Nearest Neighbor (-NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design and -NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem. Henry Joutsijoki, Markus Haponen, Jyrki Rasku, Katriina Aalto-Setälä, and Martti Juhola Copyright © 2016 Henry Joutsijoki et al. All rights reserved. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision Wed, 26 Oct 2016 12:25:46 +0000 In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method. Bineng Zhong, Shengnan Pan, Hongbo Zhang, Tian Wang, Jixiang Du, Duansheng Chen, and Liujuan Cao Copyright © 2016 Bineng Zhong et al. All rights reserved. Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy Mon, 17 Oct 2016 09:18:14 +0000 Limited-projection fluorescence molecular tomography (FMT) has short data acquisition time that allows fast resolving of the three-dimensional visualization of fluorophore within small animal in vivo. However, limited-projection FMT reconstruction suffers from severe ill-posedness because only limited projections are used for reconstruction. To alleviate the ill-posedness, a feasible region extraction strategy based on a double mesh is presented for limited-projection FMT. First, an initial result is rapidly recovered using a coarse discretization mesh. Then, the reconstructed fluorophore area in the initial result is selected as a feasible region to guide the reconstruction using a fine discretization mesh. Simulation experiments on a digital mouse and small animal experiment in vivo are performed to validate the proposed strategy. It demonstrates that the presented strategy provides a good distribution of fluorophore with limited projections of fluorescence measurements. Hence, it is suitable for reconstruction of limited-projection FMT. Huangjian Yi, Xu Zhang, Jinye Peng, Fengjun Zhao, Xiaodong Wang, Yuqing Hou, Duofang Chen, and Xiaowei He Copyright © 2016 Huangjian Yi et al. All rights reserved. Transcriptional Regulation of lncRNA Genes by Histone Modification in Alzheimer’s Disease Sun, 16 Oct 2016 13:01:14 +0000 Increasing studies have revealed that long noncoding RNAs (lncRNAs) are not transcriptional noise but play important roles in the regulation of a wide range of biological processes, and the dysregulation of lncRNA genes is associated with disease development. Alzheimer’s disease (AD) is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. However, little is known about the roles of lncRNA genes in AD and how the lncRNA genes are transcriptionally regulated. Herein, we analyzed RNA-seq data and ChIP-seq histone modification data from CK-p25 AD model and control mice and identified 72 differentially expressed lncRNA genes, 4,917 differential peaks of H3K4me3, and 1,624 differential peaks of H3K27me3 between AD and control samples, respectively. Furthermore, we found 92 differential peaks of histone modification H3K4me3 are located in the promoter of 39 differentially expressed lncRNA genes and 8 differential peaks of histone modification H3K27me3 are located upstream of 7 differentially expressed lncRNA genes, which suggest that the majority of lncRNA genes may be transcriptionally regulated by histone modification in AD. Guoqiang Wan, Wenyang Zhou, Yang Hu, Rui Ma, Shuilin Jin, Guiyou Liu, and Qinghua Jiang Copyright © 2016 Guoqiang Wan et al. All rights reserved. Modeling Gene Regulation in Liver Hepatocellular Carcinoma with Random Forests Wed, 12 Oct 2016 10:04:52 +0000 Liver hepatocellular carcinoma (HCC) remains a leading cause of cancer-related death. Poor understanding of the mechanisms underlying HCC prevents early detection and leads to high mortality. We developed a random forest model that incorporates copy-number variation, DNA methylation, transcription factor, and microRNA binding information as features to predict gene expression in HCC. Our model achieved a highly significant correlation between predicted and measured expression of held-out genes. Furthermore, we identified potential regulators of gene expression in HCC. Many of these regulators have been previously found to be associated with cancer and are differentially expressed in HCC. We also evaluated our predicted target sets for these regulators by making comparison with experimental results. Lastly, we found that the transcription factor E2F6, one of the candidate regulators inferred by our model, is predictive of survival rate in HCC. Results of this study will provide directions for future prospective studies in HCC. Hilal Kazan Copyright © 2016 Hilal Kazan. All rights reserved. Near-Infrared Fluorescence-Enhanced Optical Tomography Mon, 10 Oct 2016 15:49:32 +0000 Fluorescence-enhanced optical imaging using near-infrared (NIR) light developed for in vivo molecular targeting and reporting of cancer provides promising opportunities for diagnostic imaging. The current state of the art of NIR fluorescence-enhanced optical tomography is reviewed in the context of the principle of fluorescence, the different measurement schemes employed, and the mathematical tools established to tomographically reconstruct the fluorescence optical properties in various tissue domains. Finally, we discuss the recent advances in forward modeling and distributed memory parallel computation to provide robust, accurate, and fast fluorescence-enhanced optical tomography. Banghe Zhu and Anuradha Godavarty Copyright © 2016 Banghe Zhu and Anuradha Godavarty. All rights reserved. Potential Role of the Last Half Repeat in TAL Effectors Revealed by a Molecular Simulation Study Mon, 10 Oct 2016 09:46:51 +0000 TAL effectors (TALEs) contain a modular DNA-binding domain that is composed of tandem repeats. In all naturally occurring TALEs, the end of tandem repeats is invariantly a truncated half repeat. To investigate the potential role of the last half repeat in TALEs, we performed comparative molecular dynamics simulations for the crystal structure of DNA-bound TALE AvrBs3 lacking the last half repeat and its modeled structure having the last half repeat. The structural stability analysis indicates that the modeled system is more stable than the nonmodeled system. Based on the principle component analysis, it is found that the AvrBs3 increases its structural compactness in the presence of the last half repeat. The comparison of DNA groove parameters of the two systems implies that the last half repeat also causes the change of DNA major groove binding efficiency. The following calculation of hydrogen bond reveals that, by stabilizing the phosphate binding with DNA at the C-terminus, the last half repeat helps to adopt a compact conformation at the protein-DNA interface. It further mediates more contacts between TAL repeats and DNA nucleotide bases. Finally, we suggest that the last half repeat is required for the high-efficient recognition of DNA by TALE. Hua Wan, Shan Chang, Jian-ping Hu, Xu-hong Tian, and Mei-hua Wang Copyright © 2016 Hua Wan et al. All rights reserved. Dual-Modality Imaging of the Human Finger Joint Systems by Using Combined Multispectral Photoacoustic Computed Tomography and Ultrasound Computed Tomography Tue, 27 Sep 2016 09:38:43 +0000 We developed a homemade dual-modality imaging system that combines multispectral photoacoustic computed tomography and ultrasound computed tomography for reconstructing the structural and functional information of human finger joint systems. The fused multispectral photoacoustic-ultrasound computed tomography (MPAUCT) system was examined by the phantom and in vivo experimental tests. The imaging results indicate that the hard tissues such as the bones and the soft tissues including the blood vessels, the tendon, the skins, and the subcutaneous tissues in the finger joints systems can be effectively recovered by using our multimodality MPAUCT system. The developed MPAUCT system is able to provide us with more comprehensive information of the human finger joints, which shows its potential for characterization and diagnosis of bone or joint diseases. Yubin Liu, Yating Wang, and Zhen Yuan Copyright © 2016 Yubin Liu et al. All rights reserved. Parallel-SymD: A Parallel Approach to Detect Internal Symmetry in Protein Domains Mon, 26 Sep 2016 13:02:15 +0000 Internally symmetric proteins are proteins that have a symmetrical structure in their monomeric single-chain form. Around 10–15% of the protein domains can be regarded as having some sort of internal symmetry. In this regard, we previously published SymD (symmetry detection), an algorithm that determines whether a given protein structure has internal symmetry by attempting to align the protein to its own copy after the copy is circularly permuted by all possible numbers of residues. SymD has proven to be a useful algorithm to detect symmetry. In this paper, we present a new parallelized algorithm called Parallel-SymD for detecting symmetry of proteins on clusters of computers. The achieved speedup of the new Parallel-SymD algorithm scales well with the number of computing processors. Scaling is better for proteins with a larger number of residues. For a protein of 509 residues, a speedup of 63 was achieved on a parallel system with 100 processors. Ashwani Jha, K. M. Flurchick, Marwan Bikdash, and Dukka B. KC Copyright © 2016 Ashwani Jha et al. All rights reserved. Smoothed Norm Regularization for Sparse-View X-Ray CT Reconstruction Tue, 20 Sep 2016 16:36:47 +0000 Low-dose computed tomography (CT) reconstruction is a challenging problem in medical imaging. To complement the standard filtered back-projection (FBP) reconstruction, sparse regularization reconstruction gains more and more research attention, as it promises to reduce radiation dose, suppress artifacts, and improve noise properties. In this work, we present an iterative reconstruction approach using improved smoothed (SL0) norm regularization which is used to approximate norm by a family of continuous functions to fully exploit the sparseness of the image gradient. Due to the excellent sparse representation of the reconstruction signal, the desired tissue details are preserved in the resulting images. To evaluate the performance of the proposed SL0 regularization method, we reconstruct the simulated dataset acquired from the Shepp-Logan phantom and clinical head slice image. Additional experimental verification is also performed with two real datasets from scanned animal experiment. Compared to the referenced FBP reconstruction and the total variation (TV) regularization reconstruction, the results clearly reveal that the presented method has characteristic strengths. In particular, it improves reconstruction quality via reducing noise while preserving anatomical features. Ming Li, Cheng Zhang, Chengtao Peng, Yihui Guan, Pin Xu, Mingshan Sun, and Jian Zheng Copyright © 2016 Ming Li et al. All rights reserved. Identification and Characterization of Small Noncoding RNAs in Genome Sequences of the Edible Fungus Pleurotus ostreatus Thu, 15 Sep 2016 16:02:57 +0000 Noncoding RNAs (ncRNAs) have been identified in many fungi. However, no genome-scale identification of ncRNAs has been inventoried for basidiomycetes. In this research, we detected 254 small noncoding RNAs (sncRNAs) in a genome assembly of an isolate (CCEF00389) of Pleurotus ostreatus, which is a widely cultivated edible basidiomycetous fungus worldwide. The identified sncRNAs include snRNAs, snoRNAs, tRNAs, and miRNAs. SnRNA U1 was not found in CCEF00389 genome assembly and some other basidiomycetous genomes by BLASTn. This implies that if snRNA U1 of basidiomycetes exists, it has a sequence that varies significantly from other organisms. By analyzing the distribution of sncRNA loci, we found that snRNAs and most tRNAs (88.6%) were located in pseudo-UTR regions, while miRNAs are commonly found in introns. To analyze the evolutionary conservation of the sncRNAs in P. ostreatus, we aligned all 254 sncRNAs to the genome assemblies of some other Agaricomycotina fungi. The results suggest that most sncRNAs (77.56%) were highly conserved in P. ostreatus, and 20% were conserved in Agaricomycotina fungi. These findings indicate that most sncRNAs of P. ostreatus were not conserved across Agaricomycotina fungi. Jibin Qu, Mengran Zhao, Tom Hsiang, Xiaoxing Feng, Jinxia Zhang, and Chenyang Huang Copyright © 2016 Jibin Qu et al. All rights reserved. A Meta-Path-Based Prediction Method for Human miRNA-Target Association Thu, 15 Sep 2016 09:51:24 +0000 MicroRNAs (miRNAs) are short noncoding RNAs that play important roles in regulating gene expressing, and the perturbed miRNAs are often associated with development and tumorigenesis as they have effects on their target mRNA. Predicting potential miRNA-target associations from multiple types of genomic data is a considerable problem in the bioinformatics research. However, most of the existing methods did not fully use the experimentally validated miRNA-mRNA interactions. Here, we developed RMLM and RMLMSe to predict the relationship between miRNAs and their targets. RMLM and RMLMSe are global approaches as they can reconstruct the missing associations for all the miRNA-target simultaneously and RMLMSe demonstrates that the integration of sequence information can improve the performance of RMLM. In RMLM, we use RM measure to evaluate different relatedness between miRNA and its target based on different meta-paths; logistic regression and MLE method are employed to estimate the weight of different meta-paths. In RMLMSe, sequence information is utilized to improve the performance of RMLM. Here, we carry on fivefold cross validation and pathway enrichment analysis to prove the performance of our methods. The fivefold experiments show that our methods have higher AUC scores compared with other methods and the integration of sequence information can improve the performance of miRNA-target association prediction. Jiawei Luo, Cong Huang, and Pingjian Ding Copyright © 2016 Jiawei Luo et al. All rights reserved. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms Tue, 30 Aug 2016 14:17:40 +0000 Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge. Hong-Li Hua, Fa-Zhan Zhang, Abraham Alemayehu Labena, Chuan Dong, Yan-Ting Jin, and Feng-Biao Guo Copyright © 2016 Hong-Li Hua et al. All rights reserved. Characterization and Prediction of Protein Flexibility Based on Structural Alphabets Tue, 30 Aug 2016 12:35:34 +0000 Motivation. To assist efforts in determining and exploring the functional properties of proteins, it is desirable to characterize and predict protein flexibilities. Results. In this study, the conformational entropy is used as an indicator of the protein flexibility. We first explore whether the conformational change can capture the protein flexibility. The well-defined decoy structures are converted into one-dimensional series of letters from a structural alphabet. Four different structure alphabets, including the secondary structure in 3-class and 8-class, the PB structure alphabet (16-letter), and the DW structure alphabet (28-letter), are investigated. The conformational entropy is then calculated from the structure alphabet letters. Some of the proteins show high correlation between the conformation entropy and the protein flexibility. We then predict the protein flexibility from basic amino acid sequence. The local structures are predicted by the dual-layer model and the conformational entropy of the predicted class distribution is then calculated. The results show that the conformational entropy is a good indicator of the protein flexibility, but false positives remain a problem. The DW structure alphabet performs the best, which means that more subtle local structures can be captured by large number of structure alphabet letters. Overall this study provides a simple and efficient method for the characterization and prediction of the protein flexibility. Qiwen Dong, Kai Wang, Bin Liu, and Xuan Liu Copyright © 2016 Qiwen Dong et al. All rights reserved. ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier Sun, 28 Aug 2016 10:23:32 +0000 Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server. Daozheng Chen, Xiaoyu Tian, Bo Zhou, and Jun Gao Copyright © 2016 Daozheng Chen et al. All rights reserved. Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach Thu, 25 Aug 2016 17:06:21 +0000 Recombination presents a nonuniform distribution across the genome. Genomic regions that present relatively higher frequencies of recombination are called hotspots while those with relatively lower frequencies of recombination are recombination coldspots. Therefore, the identification of hotspots/coldspots could provide useful information for the study of the mechanism of recombination. In this study, a new computational predictor called SVM-EL was proposed to identify hotspots/coldspots across the yeast genome. It combined Support Vector Machines (SVMs) and Ensemble Learning (EL) based on three features including basic kmer (Kmer), dinucleotide-based auto-cross covariance (DACC), and pseudo dinucleotide composition (PseDNC). These features are able to incorporate the nucleic acid composition and their order information into the predictor. The proposed SVM-EL achieves an accuracy of 82.89% on a widely used benchmark dataset, which outperforms some related methods. Bingquan Liu, Yumeng Liu, and Dong Huang Copyright © 2016 Bingquan Liu et al. All rights reserved. Removing Noises Induced by Gamma Radiation in Cerenkov Luminescence Imaging Using a Temporal Median Filter Thu, 25 Aug 2016 13:27:13 +0000 Cerenkov luminescence imaging (CLI) can provide information of medical radionuclides used in nuclear imaging based on Cerenkov radiation, which makes it possible for optical means to image clinical radionuclide labeled probes. However, the exceptionally weak Cerenkov luminescence (CL) from Cerenkov radiation is susceptible to lots of impulse noises introduced by high energy gamma rays generating from the decays of radionuclides. In this work, a temporal median filter is proposed to remove this kind of impulse noises. Unlike traditional CLI collecting a single CL image with long exposure time and smoothing it using median filter, the proposed method captures a temporal sequence of CL images with shorter exposure time and employs a temporal median filter to smooth a temporal sequence of pixels. Results of in vivo experiments demonstrated that the proposed temporal median method can effectively remove random pulse noises induced by gamma radiation and achieve a robust CLI image. Xu Cao, Yang Li, Yonghua Zhan, Xueli Chen, Fei Kang, Jing Wang, and Jimin Liang Copyright © 2016 Xu Cao et al. All rights reserved. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network Thu, 25 Aug 2016 11:55:28 +0000 The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence. Yang Hu, Ying Zhang, Jun Ren, Yadong Wang, Zhenzhen Wang, and Jun Zhang Copyright © 2016 Yang Hu et al. All rights reserved. Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision Thu, 25 Aug 2016 11:17:22 +0000 Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of a target by exploiting both classic principal component analysis (PCA) algorithms with recent deep learning representation architectures. We use learned PCA eigenvectors as filters and develop a novel algorithm to represent a target by composing of a PCA-based filter bank layer, a nonlinear layer, and a patch-based pooling layer, respectively. Then, based on the feature representation, a neural network with one hidden layer is trained in a supervised mode to construct a discriminative appearance model. Finally, to alleviate the tracker drifting problem, a sample update scheme is carefully designed to keep track of the most representative and diverse samples during tracking. We test the proposed tracking method on two standard individual cell/object tracking benchmarks to show our tracker's state-of-the-art performance. Bineng Zhong, Shengnan Pan, Cheng Wang, Tian Wang, Jixiang Du, Duansheng Chen, and Liujuan Cao Copyright © 2016 Bineng Zhong et al. All rights reserved. An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction Thu, 25 Aug 2016 09:25:32 +0000 Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method. Luzhen Deng, Peng Feng, Mianyi Chen, Peng He, and Biao Wei Copyright © 2016 Luzhen Deng et al. All rights reserved. Networks Models of Actin Dynamics during Spermatozoa Postejaculatory Life: A Comparison among Human-Made and Text Mining-Based Models Wed, 24 Aug 2016 08:20:08 +0000 Here we realized a networks-based model representing the process of actin remodelling that occurs during the acquisition of fertilizing ability of human spermatozoa (HumanMade_ActinSpermNetwork, HM_ASN). Then, we compared it with the networks provided by two different text mining tools: Agilent Literature Search (ALS) and PESCADOR. As a reference, we used the data from the online repository Kyoto Encyclopaedia of Genes and Genomes (KEGG), referred to the actin dynamics in a more general biological context. We found that HM_ALS and the networks from KEGG data shared the same scale-free topology following the Barabasi-Albert model, thus suggesting that the information is spread within the network quickly and efficiently. On the contrary, the networks obtained by ALS and PESCADOR have a scale-free hierarchical architecture, which implies a different pattern of information transmission. Also, the hubs identified within the networks are different: HM_ALS and KEGG networks contain as hubs several molecules known to be involved in actin signalling; ALS was unable to find other hubs than “actin,” whereas PESCADOR gave some nonspecific result. This seems to suggest that the human-made information retrieval in the case of a specific event, such as actin dynamics in human spermatozoa, could be a reliable strategy. Nicola Bernabò, Alessandra Ordinelli, Marina Ramal Sanchez, Mauro Mattioli, and Barbara Barboni Copyright © 2016 Nicola Bernabò et al. All rights reserved. BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species Mon, 22 Aug 2016 07:29:51 +0000 MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools. Limin Jiang, Jingjun Zhang, Ping Xuan, and Quan Zou Copyright © 2016 Limin Jiang et al. All rights reserved. Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology Mon, 22 Aug 2016 07:13:42 +0000 Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analysis of genes. In this study, we presented a framework named Gene2Function to annotate Gene Reference into Functions (GeneRIFs), in which each functional description of GeneRIFs could be annotated by a text mining tool Open Biomedical Annotator (OBA), and each Entrez gene could be mapped to Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbol. After annotating all the records about human genes of GeneRIFs, 288,869 associations between 13,148 mRNAs and 7,182 terms, 9,496 associations between 948 microRNAs and 533 terms, and 901 associations between 139 long noncoding RNAs (lncRNAs) and 297 terms were obtained as a comprehensive annotation resource of human genome. High consistency of term frequency of individual gene (Pearson correlation = 0.6401, ) and gene frequency of individual term (Pearson correlation = 0.1298, ) in GeneRIFs and GOA shows our annotation resource is very reliable. Yang Hu, Wenyang Zhou, Jun Ren, Lixiang Dong, Yadong Wang, Shuilin Jin, and Liang Cheng Copyright © 2016 Yang Hu et al. All rights reserved. Uncovering Driver DNA Methylation Events in Nonsmoking Early Stage Lung Adenocarcinoma Wed, 17 Aug 2016 08:03:04 +0000 As smoking rates decrease, proportionally more cases with lung adenocarcinoma occur in never-smokers, while aberrant DNA methylation has been suggested to contribute to the tumorigenesis of lung adenocarcinoma. It is extremely difficult to distinguish which genes play key roles in tumorigenic processes via DNA methylation-mediated gene silencing from a large number of differentially methylated genes. By integrating gene expression and DNA methylation data, a pipeline combined with the differential network analysis is designed to uncover driver methylation genes and responsive modules, which demonstrate distinctive expressions and network topology in tumors with aberrant DNA methylation. Totally, 135 genes are recognized as candidate driver genes in early stage lung adenocarcinoma and top ranked 30 genes are recognized as driver methylation genes. Functional annotation and the differential network analysis indicate the roles of identified driver genes in tumorigenesis, while literature study reveals significant correlations of the top 30 genes with early stage lung adenocarcinoma in never-smokers. The analysis pipeline can also be employed in identification of driver epigenetic events for other cancers characterized by matched gene expression data and DNA methylation data. Xindong Zhang, Lin Gao, Zhi-Ping Liu, Songwei Jia, and Luonan Chen Copyright © 2016 Xindong Zhang et al. All rights reserved. Optimization to the Culture Conditions for Phellinus Production with Regression Analysis and Gene-Set Based Genetic Algorithm Tue, 16 Aug 2016 09:07:39 +0000 Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. Zhongwei Li, Yuezhen Xin, Xun Wang, Beibei Sun, Shengyu Xia, Hui Li, and Hu Zhu Copyright © 2016 Zhongwei Li et al. All rights reserved. High Dimensional Variable Selection with Error Control Mon, 15 Aug 2016 06:09:30 +0000 Background. The iterative sure independence screening (ISIS) is a popular method in selecting important variables while maintaining most of the informative variables relevant to the outcome in high throughput data. However, it not only is computationally intensive but also may cause high false discovery rate (FDR). We propose to use the FDR as a screening method to reduce the high dimension to a lower dimension as well as controlling the FDR with three popular variable selection methods: LASSO, SCAD, and MCP. Method. The three methods with the proposed screenings were applied to prostate cancer data with presence of metastasis as the outcome. Results. Simulations showed that the three variable selection methods with the proposed screenings controlled the predefined FDR and produced high area under the receiver operating characteristic curve (AUROC) scores. In applying these methods to the prostate cancer example, LASSO and MCP selected 12 and 8 genes and produced AUROC scores of 0.746 and 0.764, respectively. Conclusions. We demonstrated that the variable selection methods with the sequential use of FDR and ISIS not only controlled the predefined FDR in the final models but also had relatively high AUROC scores. Sangjin Kim and Susan Halabi Copyright © 2016 Sangjin Kim and Susan Halabi. All rights reserved. Identification of Secretory Proteins in Mycobacterium tuberculosis Using Pseudo Amino Acid Composition Thu, 11 Aug 2016 12:45:20 +0000 Tuberculosis is killing millions of lives every year and on the blacklist of the most appalling public health problems. Recent findings suggest that secretory protein of Mycobacterium tuberculosis may serve the purpose of developing specific vaccines and drugs due to their antigenicity. Responding to global infectious disease, we focused on the identification of secretory proteins in Mycobacterium tuberculosis. A novel method called MycoSec was designed by incorporating -gap dipeptide compositions into pseudo amino acid composition. Analysis of variance-based technique was applied in the process of feature selection and a total of 374 optimal features were obtained and used for constructing the final predicting model. In the jackknife test, MycoSec yielded a good performance with the area under the receiver operating characteristic curve of 0.93, demonstrating that the proposed system is powerful and robust. For user’s convenience, the web server MycoSec was established and an obliging manual on how to use it was provided for getting around any trouble unnecessary. Huan Yang, Hua Tang, Xin-Xin Chen, Chang-Jian Zhang, Pan-Pan Zhu, Hui Ding, Wei Chen, and Hao Lin Copyright © 2016 Huan Yang et al. All rights reserved. A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network Tue, 09 Aug 2016 07:33:48 +0000 Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL. Zhongwei Li, Beibei Sun, Yuezhen Xin, Xun Wang, and Hu Zhu Copyright © 2016 Zhongwei Li et al. All rights reserved. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches Mon, 08 Aug 2016 06:10:29 +0000 Gamma-aminobutyric acid type-A receptors (s) belong to multisubunit membrane spanning ligand-gated ion channels (LGICs) which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of s just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins’ physicochemical properties, amino acids composition and position, a classifier was first constructed using a 188-dimensional (188D) algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC) and ProtrWeb web-based algorithms for human proteins. Then, four classifiers including gradient boosting decision tree (GBDT), random forest (RF), a library for support vector machine (libSVM), and k-nearest neighbor (-NN) were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew’s correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and -NN at the second dataset. In conclusion, a classifier was successfully constructed using only the protein sequence information. Zhijun Liao, Yong Huang, Xiaodong Yue, Huijuan Lu, Ping Xuan, and Ying Ju Copyright © 2016 Zhijun Liao et al. All rights reserved. Positive-Unlabeled Learning for Pupylation Sites Prediction Sun, 07 Aug 2016 07:43:02 +0000 Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-consuming and labor-sensitive. With the existing computational methods, the experimentally annotated pupylation sites are used as the positive training set and the remaining nonannotated lysine residues as the negative training set to build classifiers to predict new pupylation sites from the unknown proteins. However, the remaining nonannotated lysine residues may contain pupylation sites which have not been experimentally validated yet. Unlike previous methods, in this study, the experimentally annotated pupylation sites were used as the positive training set whereas the remaining nonannotated lysine residues were used as the unlabeled training set. A novel method named PUL-PUP was proposed to predict pupylation sites by using positive-unlabeled learning technique. Our experimental results indicated that PUL-PUP outperforms the other methods significantly for the prediction of pupylation sites. As an application, PUL-PUP was also used to predict the most likely pupylation sites in nonannotated lysine sites. Ming Jiang and Jun-Zhe Cao Copyright © 2016 Ming Jiang and Jun-Zhe Cao. All rights reserved. Comparison of FDA Approved Kinase Targets to Clinical Trial Ones: Insights from Their System Profiles and Drug-Target Interaction Networks Thu, 28 Jul 2016 07:56:16 +0000 Kinase is one of the most productive classes of established targets, but the majority of approved drugs against kinase were developed only for cancer. Intensive efforts were therefore exerted for releasing its therapeutic potential by discovering new therapeutic area. Kinases in clinical trial could provide great opportunities for treating various diseases. However, no systematic comparison between system profiles of established targets and those of clinical trial ones was conducted. The reveal of probable difference or shift of trend would help to identify key factors defining druggability of established targets. In this study, a comparative analysis of system profiles of both types of targets was conducted. Consequently, the systems profiles of the majority of clinical trial kinases were identified to be very similar to those of established ones, but percentages of established targets obeying the system profiles appeared to be slightly but consistently higher than those of clinical trial targets. Moreover, a shift of trend in the system profiles from the clinical trial to the established targets was identified, and popular kinase targets were discovered. In sum, this comparative study may help to facilitate the identification of the druggability of established drug targets by their system profiles and drug-target interaction networks. Jingyu Xu, Panpan Wang, Hong Yang, Jin Zhou, Yinghong Li, Xiaoxu Li, Weiwei Xue, Chunyan Yu, Yubin Tian, and Feng Zhu Copyright © 2016 Jingyu Xu et al. All rights reserved. Constructing Phylogenetic Networks Based on the Isomorphism of Datasets Thu, 28 Jul 2016 06:41:00 +0000 Constructing rooted phylogenetic networks from rooted phylogenetic trees has become an important problem in molecular evolution. So far, many methods have been presented in this area, in which most efficient methods are based on the incompatible graph, such as the CASS, the LNETWORK, and the BIMLR. This paper will research the commonness of the methods based on the incompatible graph, the relationship between incompatible graph and the phylogenetic network, and the topologies of incompatible graphs. We can find out all the simplest datasets for a topology and construct a network for every dataset. For any one dataset , we can compute a network from the network representing the simplest dataset which is isomorphic to . This process will save more time for the algorithms when constructing networks. Juan Wang, Zhibin Zhang, and Yanjuan Li Copyright © 2016 Juan Wang et al. All rights reserved. Human Ribosomal RNA-Derived Resident MicroRNAs as the Transmitter of Information upon the Cytoplasmic Cancer Stress Tue, 19 Jul 2016 11:23:55 +0000 Dysfunction of ribosome biogenesis induces divergent ribosome-related diseases including ribosomopathy and occasionally results in carcinogenesis. Although many defects in ribosome-related genes have been investigated, little is known about contribution of ribosomal RNA (rRNA) in ribosome-related disorders. Meanwhile, microRNA (miRNA), an important regulator of gene expression, is derived from both coding and noncoding region of the genome and is implicated in various diseases. Therefore, we performed in silico analyses using M-fold, TargetScan, GeneCoDia3, and so forth to investigate RNA relationships between rRNA and miRNA against cellular stresses. We have previously shown that miRNA synergism is significantly correlated with disease and the miRNA package is implicated in memory for diseases; therefore, quantum Dynamic Nexus Score (DNS) was also calculated using MESer program. As a result, seventeen RNA sequences identical with known miRNAs were detected in the human rRNA and termed as rRNA-hosted miRNA analogs (rmiRNAs). Eleven of them were predicted to form stem-loop structures as pre-miRNAs, and especially one stem-loop was completely identical with hsa-pre-miR-3678 located in the non-rDNA region. Thus, these rmiRNAs showed significantly high DNS values, participation in regulation of cancer-related pathways, and interaction with nucleolar RNAs, suggesting that rmiRNAs may be stress-responsible resident miRNAs which transmit stress-tuning information in multiple levels. Masaru Yoshikawa and Yoichi Robertus Fujii Copyright © 2016 Masaru Yoshikawa and Yoichi Robertus Fujii. All rights reserved. Gene-Disease Interaction Retrieval from Multiple Sources: A Network Based Method Wed, 13 Jul 2016 13:25:44 +0000 The number of gene-related databases has been growing largely along with the research on genes of bioinformatics. Those databases are filled with various gene functions, pathways, interactions, and so forth, while much biomedical knowledge about human diseases is stored as text in all kinds of literatures. Researchers have developed many methods to extract structured biomedical knowledge. Some study and improve text mining algorithms to achieve efficiency in order to cover as many data sources as possible, while some build open source database to accept individual submissions in order to achieve accuracy. This paper combines both efforts and biomedical ontologies to build an interaction network of multiple biomedical ontologies, which guarantees its robustness as well as its wide coverage of biomedical publications. Upon the network, we accomplish an algorithm which discovers paths between concept pairs and shows potential relations. Lan Huang, Ye Wang, Yan Wang, and Tian Bai Copyright © 2016 Lan Huang et al. All rights reserved. An Ultrasound Simulation Model for the Pulsatile Blood Flow Modulated by the Motion of Stenosed Vessel Wall Sun, 10 Jul 2016 07:29:48 +0000 This paper presents an ultrasound simulation model for pulsatile blood flow, modulated by the motion of a stenosed vessel wall. It aims at generating more realistic ultrasonic signals to provide an environment for evaluating ultrasound signal processing and imaging and a framework for investigating the behaviors of blood flow field modulated by wall motion. This model takes into account fluid-structure interaction, blood pulsatility, stenosis of the vessel, and arterial wall movement caused by surrounding tissue’s motion. The axial and radial velocity distributions of blood and the displacement of vessel wall are calculated by solving coupled Navier-Stokes and wall equations. With these obtained values, we made several different phantoms by treating blood and the vessel wall as a group of point scatterers. Then, ultrasound echoed signals from oscillating wall and blood in the axisymmetric stenotic-carotid arteries were computed by ultrasound simulation software, Field II. The results show better consistency with corresponding theoretical values and clinical data and reflect the influence of wall movement on the flow field. It can serve as an effective tool not only for investigating the behavior of blood flow field modulated by wall motion but also for quantitative or qualitative evaluation of new ultrasound imaging technology and estimation method of blood velocity. Qinghui Zhang, Yufeng Zhang, Yi Zhou, Kun Zhang, Kexin Zhang, and Lian Gao Copyright © 2016 Qinghui Zhang et al. All rights reserved. Prognostic Value of Osteopontin Splice Variant-c Expression in Breast Cancers: A Meta-Analysis Mon, 04 Jul 2016 07:44:40 +0000 Objectives. Osteopontin (OPN) is overexpressed in breast cancers, while its clinical and prognostic significance remained unclear. This study aimed to assess the prognostic value of OPN, especially its splice variants, in breast cancers. Methods. Data were extracted from eligible studies concerning the OPN and OPN-c expression in breast cancer patients and were used to calculate the association between OPN/OPN-c and survival. Two reviewer teams independently screened the literatures according to the inclusion and exclusion criteria based on quality evaluation. Following the processes of data extraction, assessment, and transformation, meta-analysis was carried out via RevMan 5.3 software. Results. A total of ten studies involving 1,567 patients were included. The results demonstrated that high level OPN indicated a poor outcome in the OS (HR = 2.22, 95% CI: 1.23–4.00, and ; random-effects model) with heterogeneity (%) of breast cancer patients. High level OPN-c appeared to be more significantly associated with poor survival (HR = 2.14, 95% CI: 1.51–3.04, and ; fixed-effects model) with undetected heterogeneity (%). Conclusions. Our analyses indicated that both OPN and OPN-c could be considered as prognostic markers for breast cancers. The high level of OPN-c was suggested to be more reliably associated with poor survival in breast cancer patients. Chengcheng Hao, Zhiyan Wang, Yanan Gu, Wen G. Jiang, and Shan Cheng Copyright © 2016 Chengcheng Hao et al. All rights reserved. Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM Wed, 29 Jun 2016 16:30:15 +0000 Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems. Zhen-Guo Gao, Lei Wang, Shi-Xiong Xia, Zhu-Hong You, Xin Yan, and Yong Zhou Copyright © 2016 Zhen-Guo Gao et al. All rights reserved. Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition Wed, 29 Jun 2016 09:22:54 +0000 Owing to the abuse of antibiotics, drug resistance of pathogenic bacteria becomes more and more serious. Therefore, it is interesting to develop a more reasonable way to solve this issue. Because they can destroy the bacterial cell structure and then kill the infectious bacterium, the bacterial cell wall lyases are suitable candidates of antibacteria sources. Thus, it is urgent to develop an accurate and efficient computational method to predict the lyases. Based on the consideration, in this paper, a set of objective and rigorous data was collected by searching through the Universal Protein Resource (the UniProt database), whereafter a feature selection technique based on the analysis of variance (ANOVA) was used to acquire optimal feature subset. Finally, the support vector machine (SVM) was used to perform prediction. The jackknife cross-validated results showed that the optimal average accuracy of 84.82% was achieved with the sensitivity of 76.47% and the specificity of 93.16%. For the convenience of other scholars, we built a free online server called Lypred. We believe that Lypred will become a practical tool for the research of cell wall lyases and development of antimicrobial agents. Xin-Xin Chen, Hua Tang, Wen-Chao Li, Hao Wu, Wei Chen, Hui Ding, and Hao Lin Copyright © 2016 Xin-Xin Chen et al. All rights reserved. Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq Data Mon, 27 Jun 2016 16:15:54 +0000 Many disease-related single nucleotide polymorphisms (SNPs) have been inferred from genome-wide association studies (GWAS) in recent years. Numerous studies have shown that some SNPs located in protein-coding regions are associated with numerous diseases by affecting gene expression. However, in noncoding regions, the mechanism of how SNPs contribute to disease susceptibility remains unclear. Enhancer elements are functional segments of DNA located in noncoding regions that play an important role in regulating gene expression. The SNPs located in enhancer elements may affect gene expression and lead to disease. We presented a method for identifying liver cancer-related enhancer SNPs through integrating GWAS and histone modification ChIP-seq data. We identified 22 liver cancer-related enhancer SNPs, 9 of which were regulatory SNPs involved in distal transcriptional regulation. The results highlight that these enhancer SNPs may play important roles in liver cancer. Tianjiao Zhang, Yang Hu, Xiaoliang Wu, Rui Ma, Qinghua Jiang, and Yadong Wang Copyright © 2016 Tianjiao Zhang et al. All rights reserved. A Metric on the Space of Partly Reduced Phylogenetic Networks Thu, 23 Jun 2016 06:48:16 +0000 Phylogenetic networks are a generalization of phylogenetic trees that allow for the representation of evolutionary events acting at the population level, such as recombination between genes, hybridization between lineages, and horizontal gene transfer. The researchers have designed several measures for computing the dissimilarity between two phylogenetic networks, and each measure has been proven to be a metric on a special kind of phylogenetic networks. However, none of the existing measures is a metric on the space of partly reduced phylogenetic networks. In this paper, we provide a metric, -distance, on the space of partly reduced phylogenetic networks, which is polynomial-time computable. Juan Wang Copyright © 2016 Juan Wang. All rights reserved. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm Wed, 15 Jun 2016 09:22:51 +0000 The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. Zhang Yang, Ye Shufan, Guo Li, and Ding Weifeng Copyright © 2016 Zhang Yang et al. All rights reserved. Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines Mon, 13 Jun 2016 06:14:01 +0000 Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification. Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs. Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination. Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions. Joanna Jaworek-Korjakowska Copyright © 2016 Joanna Jaworek-Korjakowska. All rights reserved. Mathematical Based Calculation of Drug Penetration Depth in Solid Tumors Wed, 08 Jun 2016 08:26:31 +0000 Cancer is a class of diseases characterized by out-of-control cells’ growth which affect cells and make them damaged. Many treatment options for cancer exist. Chemotherapy as an important treatment option is the use of drugs to treat cancer. The anticancer drug travels to the tumor and then diffuses in it through capillaries. The diffusion of drugs in the solid tumor is limited by penetration depth which is different in case of different drugs and cancers. The computation of this depth is important as it helps physicians to investigate about treatment of infected tissue. Although many efforts have been made on studying and measuring drug penetration depth, less works have been done on computing this length from a mathematical point of view. In this paper, first we propose phase lagging model for diffusion of drug in the tumor. Then, using this model on one side and considering the classic diffusion on the other side, we compute the drug penetration depth in the solid tumor. This computed value of drug penetration depth is corroborated by comparison with the values measured by experiments. Hamidreza Namazi, Vladimir V. Kulish, Albert Wong, and Sina Nazeri Copyright © 2016 Hamidreza Namazi et al. All rights reserved. A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information Tue, 31 May 2016 08:18:03 +0000 MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter in the weight formula. By changing value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules. Zhao Li, Yilei Zhao, Gaofeng Pan, Jijun Tang, and Fei Guo Copyright © 2016 Zhao Li et al. All rights reserved. Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences Mon, 23 May 2016 11:06:05 +0000 We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research. Ji-Yong An, Fan-Rong Meng, Zhu-Hong You, Yu-Hong Fang, Yu-Jun Zhao, and Ming Zhang Copyright © 2016 Ji-Yong An et al. All rights reserved. Protein Remote Homology Detection Based on an Ensemble Learning Approach Sun, 08 May 2016 10:51:22 +0000 Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods. Junjie Chen, Bingquan Liu, and Dong Huang Copyright © 2016 Junjie Chen et al. All rights reserved. Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study Wed, 04 May 2016 08:47:34 +0000 Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H+-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction. Nachon Raethong, Jirasak Wong-ekkabut, Kobkul Laoteng, and Wanwipa Vongsangnak Copyright © 2016 Nachon Raethong et al. All rights reserved. Guitar: An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features Thu, 28 Apr 2016 12:17:57 +0000 Biological features, such as genes and transcription factor binding sites, are often denoted with genome-based coordinates as the genomic features. While genome-based representation is usually very effective in correlating various biological features, it can be tedious to examine the relationship between RNA-related genomic features and the landmarks of RNA transcripts with existing tools due to the difficulty in the conversion between genome-based coordinates and RNA-based coordinates. We developed here an open source Guitar R/Bioconductor package for sketching the transcriptomic view of RNA-related biological features represented by genome based coordinates. Internally, Guitar package extracts the standardized RNA coordinates with respect to the landmarks of RNA transcripts, with which hundreds of millions of RNA-related genomic features can then be efficiently analyzed within minutes. We demonstrated the usage of Guitar package in analyzing posttranscriptional RNA modifications (5-methylcytosine and N6-methyladenosine) derived from high-throughput sequencing approaches (MeRIP-Seq and RNA BS-Seq) and show that RNA 5-methylcytosine (m5C) is enriched in 5′UTR. The newly developed Guitar R/Bioconductor package achieves stable performance on the data tested and revealed novel biological insights. It will effectively facilitate the analysis of RNA methylation data and other RNA-related biological features in the future. Xiaodong Cui, Zhen Wei, Lin Zhang, Hui Liu, Lei Sun, Shao-Wu Zhang, Yufei Huang, and Jia Meng Copyright © 2016 Xiaodong Cui et al. All rights reserved. Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery Tue, 05 Apr 2016 12:32:44 +0000 Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept. During a training phase, a large number of image frames with known tissue types are analyzed and the most discriminant image-based signatures for various tissue types are identified. During the procedure, the algorithm uses the learnt image features to assign a proper tissue type to the acquired image frame. We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma. The algorithm was trained using 117 image sequences containing over 27 thousand images captured from more than 20 patients. We achieved an average cross validation accuracy of better than 83%. We believe this algorithm could be a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information. Ali Kamen, Shanhui Sun, Shaohua Wan, Stefan Kluckner, Terrence Chen, Alexander M. Gigler, Elfriede Simon, Maximilian Fleischer, Mehreen Javed, Samira Daali, Alhadi Igressa, and Patra Charalampaki Copyright © 2016 Ali Kamen et al. All rights reserved. An Efficient Approach to Screening Epigenome-Wide Data Sun, 13 Mar 2016 06:29:06 +0000 Screening cytosine-phosphate-guanine dinucleotide (CpG) DNA methylation sites in association with some covariate(s) is desired due to high dimensionality. We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for nested validation to screen CpG sites. SVA is to account for variations in the methylation not explained by the specified covariate(s) and adjust for confounding effects. To make it easier to users, this screening method is built into a user-friendly R package, ttScreening, with efficient algorithms implemented. Various simulations were implemented to examine the robustness and sensitivity of the method compared to the classical approaches controlling for multiple testing: the false discovery rates-based (FDR-based) and the Bonferroni-based methods. The proposed approach in general performs better and has the potential to control both types I and II errors. We applied ttScreening to 383,998 CpG sites in association with maternal smoking, one of the leading factors for cancer risk. Meredith A. Ray, Xin Tong, Gabrielle A. Lockett, Hongmei Zhang, and Wilfried J. J. Karmaus Copyright © 2016 Meredith A. Ray et al. All rights reserved. Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms Mon, 29 Feb 2016 18:54:28 +0000 Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn) and average stride interval (ASI) parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney test () in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth. The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years), middle (aged 6–8 years), and elder (aged 10–14 years) children. Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems. In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%), recall (≥0.8), and precision (≥0.8077). Meihong Wu, Lifang Liao, Xin Luo, Xiaoquan Ye, Yuchen Yao, Pinnan Chen, Lei Shi, Hui Huang, and Yunfeng Wu Copyright © 2016 Meihong Wu et al. All rights reserved. Pacemaker Created in Human Ventricle by Depressing Inward-Rectifier K+ Current: A Simulation Study Sun, 21 Feb 2016 10:17:19 +0000 Cardiac conduction disorders are common diseases which cause slow heart rate and syncope. The best way to treat these diseases by now is to implant electronic pacemakers, which, yet, have many disadvantages, such as the limited battery life and infection. Biopacemaker has been expected to replace the electronic devices. Automatic ventricular myocytes (VMs) could show pacemaker activity, which was induced by depressing inward-rectifier K+ current (). In this study, a 2D model of human biopacemaker was created from the ventricular endocardial myocytes. We examined the stability of the created biopacemaker and investigated its driving capability by finding the suitable size and spatial distribution of the pacemaker for robust pacing and driving the surrounding quiescent cardiomyocytes. Our results suggest that the rhythm of the pacemaker is similar to that of the single cell at final stable state. The driving force of the biopacemaker is closely related to the pattern of spatial distribution of the pacemaker. Yue Zhang, Kuanquan Wang, Qince Li, and Henggui Zhang Copyright © 2016 Yue Zhang et al. All rights reserved. Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence Sun, 17 Jan 2016 16:24:19 +0000 Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision. Joanna Jaworek-Korjakowska and Paweł Kłeczek Copyright © 2016 Joanna Jaworek-Korjakowska and Paweł Kłeczek. All rights reserved. Long Read Alignment with Parallel MapReduce Cloud Platform Tue, 29 Dec 2015 07:26:39 +0000 Genomic sequence alignment is an important technique to decode genome sequences in bioinformatics. Next-Generation Sequencing technologies produce genomic data of longer reads. Cloud platforms are adopted to address the problems arising from storage and analysis of large genomic data. Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation. However, serial execution of map and reduce phases is a problem in such systems. Therefore, in this paper, we introduce Burrows-Wheeler Aligner’s Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) cloud platform for long sequence alignment. The proposed cloud platform adopts a widely accepted and accurate BWA-SW algorithm for long sequence alignment. A custom MapReduce platform is developed to overcome the drawbacks of the Hadoop framework. A parallel execution strategy of the MapReduce phases and optimization of Smith-Waterman algorithm are considered. Performance evaluation results exhibit an average speed-up of 6.7 considering BWASW-PMR compared with the state-of-the-art Bwasw-Cloud. An average reduction of 30% in the map phase makespan is reported across all experiments comparing BWASW-PMR with Bwasw-Cloud. Optimization of Smith-Waterman results in reducing the execution time by 91.8%. The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms. Ahmed Abdulhakim Al-Absi and Dae-Ki Kang Copyright © 2015 Ahmed Abdulhakim Al-Absi and Dae-Ki Kang. All rights reserved. Comment on “Transmission Model of Hepatitis B Virus with Migration Effect” Thu, 17 Dec 2015 12:54:05 +0000 We show the erroneous assumptions and reasoning by introducing the migration effect of individuals in the transmission model of Hepatitis B virus. First, some false results related to the eigenvalues and reproductive number in the recent literature in mathematical biology will be presented. Then, it will be proved that the product of the matrices in the next generation method to obtain the reproductive number is not correct and the local and global stability results based on the reproductive number are considered false. Anwar Zeb and Gul Zaman Copyright © 2015 Anwar Zeb and Gul Zaman. All rights reserved. A Kinetic-Model-Based Approach to Identify Malfunctioning Components in Signal Transduction Pathways from Artificial Clinical Data Sun, 29 Nov 2015 07:30:56 +0000 Detection of malfunctioning reactions or molecules from clinical data is essential for disease treatments. In order to find an alternative to the existing oversimplistic mathematical models, a kinetic model is developed in this work to infer the malfunctioning reactions/molecules by quantifying the similarity between the clinical profile and the output profiles predicted from the model in which certain reactions/molecules malfunction. The new approach was tested in IL-6 and TNF-α/NF-κB signaling pathway, for four abnormal conditions including up/downregulation of single reaction rate constants and up/downregulation of single molecules. Since limited quantitative clinical data were available, the IL-6 ODE model was used to generate artificial clinical data for the abnormal steady-state value shown in two key molecules: nuclear STAT3 and SOCS3. Similarly, the TNF-α/NF-κB model was used to obtain the data in which abnormal oscillation dynamic was shown in the profile of NF-κB. The results show that the approach developed in this study was able to successfully identify the malfunctioning reactions and molecules from the clinical data. It was also found that this new approach was noise-robust and that it managed to reveal unique solution for the faulty components in a network. Xianhua Li, Nicholas Ribaudo, and Zuyi (Jacky) Huang Copyright © 2015 Xianhua Li et al. All rights reserved. Comment on “Simulating Radiotherapy Effect in High-Grade Glioma by Using Diffusive Modeling and Brain Atlases” Sun, 22 Nov 2015 09:53:36 +0000 Giovanni Borasi and Alan E. Nahum Copyright © 2015 Giovanni Borasi and Alan E. Nahum. All rights reserved. Source-Space Cross-Frequency Amplitude-Amplitude Coupling in Tinnitus Thu, 19 Nov 2015 07:14:21 +0000 The thalamocortical dysrhythmia (TCD) model has been influential in the development of theoretical explanations for the neurological mechanisms of tinnitus. It asserts that thalamocortical oscillations lock a region in the auditory cortex into an ectopic slow-wave theta rhythm (4–8 Hz). The cortical area surrounding this region is hypothesized to generate abnormal gamma (>30 Hz) oscillations (“edge effect”) giving rise to the tinnitus percept. Consequently, the model predicts enhanced cross-frequency coherence in a broad range between theta and gamma. In this magnetoencephalography study involving tinnitus and control cohorts, we investigated this prediction. Using beamforming, cross-frequency amplitude-amplitude coupling (AAC) was computed within the auditory cortices for frequencies () between 2 and 80 Hz. We find the AAC signal to decompose into two distinct components at low ( Hz) and high ( Hz) frequencies, respectively. Studying the correlation of AAC with several key covariates (age, hearing level (HL), tinnitus handicap and duration, and HL at tinnitus frequency), we observe a statistically significant association between age and low-frequency AAC. Contrary to the TCD predictions, however, we do not find any indication of statistical differences in AAC between tinnitus and controls and thus no evidence for the predicted enhancement of cross-frequency coupling in tinnitus. Oliver Zobay and Peyman Adjamian Copyright © 2015 Oliver Zobay and Peyman Adjamian. All rights reserved. Simulations of Heart Function Thu, 12 Nov 2015 09:07:04 +0000 Rodrigo Weber dos Santos, Sergio Alonso, Elizabeth M. Cherry, and Joakim Sundnes Copyright © 2015 Rodrigo Weber dos Santos et al. All rights reserved. Variations in the Intragene Methylation Profiles Hallmark Induced Pluripotency Thu, 05 Nov 2015 11:11:44 +0000 We demonstrate the potential of differentiating embryonic and induced pluripotent stem cells by the regularized linear and decision tree machine learning classification algorithms, based on a number of intragene methylation measures. The resulting average accuracy of classification has been proven to be above 95%, which overcomes the earlier achievements. We propose a constructive and transparent method of feature selection based on classifier accuracy. Enrichment analysis reveals statistically meaningful presence of stemness group and cancer discriminating genes among the selected best classifying features. These findings stimulate the further research on the functional consequences of these differences in methylation patterns. The presented approach can be broadly used to discriminate the cells of different phenotype or in different state by their methylation profiles, identify groups of genes constituting multifeature classifiers, and assess enrichment of these groups by the sets of genes with a functionality of interest. Pavel Druzhkov, Nikolay Zolotykh, Iosif Meyerov, Ahmed Alsaedi, Maria Shutova, Mikhail Ivanchenko, and Alexey Zaikin Copyright © 2015 Pavel Druzhkov et al. All rights reserved. Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures Wed, 28 Oct 2015 07:20:11 +0000 Fast increasing computational demand for all-to-all protein structures comparison (PSC) is a result of three confounding factors: rapidly expanding structural proteomics databases, high computational complexity of pairwise protein comparison algorithms, and the trend in the domain towards using multiple criteria for protein structures comparison (MCPSC) and combining results. We have developed a software framework that exploits many-core and multicore CPUs to implement efficient parallel MCPSC in modern processors based on three popular PSC methods, namely, TMalign, CE, and USM. We evaluate and compare the performance and efficiency of the two parallel MCPSC implementations using Intel’s experimental many-core Single-Chip Cloud Computer (SCC) as well as Intel’s Core i7 multicore processor. We show that the 48-core SCC is more efficient than the latest generation Core i7, achieving a speedup factor of 42 (efficiency of 0.9), making many-core processors an exciting emerging technology for large-scale structural proteomics. We compare and contrast the performance of the two processors on several datasets and also show that MCPSC outperforms its component methods in grouping related domains, achieving a high -measure of 0.91 on the benchmark CK34 dataset. The software implementation for protein structure comparison using the three methods and combined MCPSC, along with the developed underlying rckskel algorithmic skeletons library, is available via GitHub. Anuj Sharma and Elias S. Manolakos Copyright © 2015 Anuj Sharma and Elias S. Manolakos. All rights reserved. Basis for the Induction of Tissue-Level Phase-2 Reentry as a Repolarization Disorder in the Brugada Syndrome Sun, 25 Oct 2015 08:37:27 +0000 Aims. Human action potentials in the Brugada syndrome have been characterized by delayed or even complete loss of dome formation, especially in the right ventricular epicardial layers. Such a repolarization pattern is believed to trigger phase-2 reentry (P2R); however, little is known about the conditions necessary for its initiation. This study aims to determine the specific mechanisms that facilitate P2R induction in Brugada-affected cardiac tissue in humans. Methods. Ionic models for Brugada syndrome in human epicardial cells were developed and used to study the induction of P2R in cables, sheets, and a three-dimensional model of the right ventricular free wall. Results. In one-dimensional cables, P2R can be induced by adjoining lost-dome and delayed-dome regions, as mediated by tissue excitability and transmembrane voltage profiles, and reduced coupling facilitates its induction. In two and three dimensions, sustained reentry can arise when three regions (delayed-dome, lost-dome, and normal epicardium) are present. Conclusions. Not only does P2R induction by Brugada syndrome require regions of action potential with delayed-dome and lost-dome, but in order to generate a sustained reentry from a triggered waveback multiple factors are necessary, including heterogeneity in action potential distribution, tissue coupling, direction of stimulation, the shape of the late plateau, the duration of lost-dome action potentials, and recovery of tissue excitability, which is predominantly modulated by tissue coupling. Alfonso Bueno-Orovio, Elizabeth M. Cherry, Steven J. Evans, and Flavio H. Fenton Copyright © 2015 Alfonso Bueno-Orovio et al. All rights reserved. Quantitative Image Analysis of Epithelial and Stromal Area in Histological Sections of Colorectal Cancer: An Emerging Diagnostic Tool Thu, 22 Oct 2015 09:37:19 +0000 In colorectal cancer (CRC), an increase in the stromal (S) area with the reduction of the epithelial (E) parts has been suggested as an indication of tumor progression. Therefore, an automated image method capable of discriminating E and S areas would allow an improved diagnosis. Immunofluorescence staining was performed on paraffin-embedded sections from colorectal tumors (16 samples from patients with liver metastasis and 18 without). Noncancerous tumor adjacent mucosa () and normal mucosa () were taken as controls. Epithelial cells were identified by an anti-keratin 8 (K8) antibody. Large tissue areas (5–63 mm2/slide) including tumor center, tumor front, and adjacent mucosa were scanned using an automated microscopy system (TissueFAXS). With our newly developed algorithms, we showed that there is more K8-immunoreactive E in the tumor center than in tumor adjacent and normal mucosa. Comparing patients with and without metastasis, the E/S ratio decreased by 20% in the tumor center and by 40% at tumor front in metastatic samples. The reduction of E might be due to a more aggressive phenotype in metastasis patients. The novel software allowed a detailed morphometric analysis of cancer tissue compartments as tools for objective quantitative measurements, reduced analysis time, and increased reproducibility of the data. R. Rogojanu, T. Thalhammer, U. Thiem, A. Heindl, I. Mesteri, A. Seewald, W. Jäger, C. Smochina, I. Ellinger, and G. Bises Copyright © 2015 R. Rogojanu et al. All rights reserved. An Electromechanical Left Ventricular Wedge Model to Study the Effects of Deformation on Repolarization during Heart Failure Thu, 15 Oct 2015 13:35:31 +0000 Heart failure is a major and costly problem in public health, which, in certain cases, may lead to death. The failing heart undergo a series of electrical and structural changes that provide the underlying basis for disturbances like arrhythmias. Computer models of coupled electrical and mechanical activities of the heart can be used to advance our understanding of the complex feedback mechanisms involved. In this context, there is a lack of studies that consider heart failure remodeling using strongly coupled electromechanics. We present a strongly coupled electromechanical model to study the effects of deformation on a human left ventricle wedge considering normal and hypertrophic heart failure conditions. We demonstrate through a series of simulations that when a strongly coupled electromechanical model is used, deformation results in the thickening of the ventricular wall that in turn increases transmural dispersion of repolarization. These effects were analyzed in both normal and failing heart conditions. We also present transmural electrograms obtained from these simulations. Our results suggest that the waveform of electrograms, particularly the T-wave, is influenced by cardiac contraction on both normal and pathological conditions. B. M. Rocha, E. M. Toledo, L. P. S. Barra, and R. Weber dos Santos Copyright © 2015 B. M. Rocha et al. All rights reserved. Modeling the Effects of Multiple Intervention Strategies on Controlling Foot-and-Mouth Disease Sun, 04 Oct 2015 14:44:14 +0000 Foot-and-mouth disease (FMD) is a threat to economic security and infrastructure as well as animal health, in both developed and developing countries. We propose and analyze an optimal control problem where the control system is a mathematical model for FMD that incorporates vaccination and culling of infectious animals. The control functions represent the fraction of animals that are vaccinated during an outbreak, infectious symptomatic animals that are detected and culled, and infectious nonsymptomatic animals that are detected and culled. Our aim was to study how these control measures should be implemented for a certain time period, in order to reduce or eliminate FMD in the community, while minimizing the interventions implementation costs. A cost-effectiveness analysis is carried out, to compare the application of each one of the control measures, separately or in combination. Steady Mushayabasa and Gift Tapedzesa Copyright © 2015 Steady Mushayabasa and Gift Tapedzesa. All rights reserved. Multiphysics and Multiscale Analysis for Chemotherapeutic Drug Mon, 28 Sep 2015 13:49:29 +0000 This paper presents a three-dimensional dynamic model for the chemotherapy design based on a multiphysics and multiscale approach. The model incorporates cancer cells, matrix degrading enzymes (MDEs) secreted by cancer cells, degrading extracellular matrix (ECM), and chemotherapeutic drug. Multiple mechanisms related to each component possible in chemotherapy are systematically integrated for high reliability of computational analysis of chemotherapy. Moreover, the fidelity of the estimated efficacy of chemotherapy is enhanced by atomic information associated with the diffusion characteristics of chemotherapeutic drug, which is obtained from atomic simulations. With the developed model, the invasion process of cancer cells in chemotherapy treatment is quantitatively investigated. The performed simulations suggest a substantial potential of the presented model for a reliable design technology of chemotherapy treatment. Linan Zhang, Sung Youb Kim, and Dongchoul Kim Copyright © 2015 Linan Zhang et al. All rights reserved. Characterization of Closed Head Impact Injury in Rat Wed, 16 Sep 2015 13:15:01 +0000 The closed head impact (CHI) rat models are commonly used for studying the traumatic brain injury. The impact parameters vary considerably among different laboratories, making the comparison of research findings difficult. In this work, numerical CHI experiments were conducted to investigate the sensitivities of intracranial responses to various impact parameters (e.g., impact depth, velocity, and position; impactor diameter, material, and shape). A three-dimensional finite element rat head model with anatomical details was subjected to impact loadings. Results revealed that impact depth and impactor shape were the two leading factors affecting intracranial responses. The influence of impactor diameter was region-specific and an increase in impactor diameter could substantially increase tissue strains in the region which located directly beneath the impactor. The lateral impact could induce higher strains in the brain than the central impact. An indentation depth instead of impact depth would be appropriate to characterize the influence of a large deformed rubber impactor. The experimentally observed velocity-dependent injury severity could be attributed to the “overshoot” phenomenon. This work could be used to better design or compare CHI experiments. Yi Hua, Praveen Akula, Matthew Kelso, and Linxia Gu Copyright © 2015 Yi Hua et al. All rights reserved. Comment on “Transmission Model of Hepatitis B Virus with the Migration Effect” Sun, 30 Aug 2015 13:53:53 +0000 Some consequences of erroneous results concerning eigenvalues in the recent literature of mathematical biology are highlighted. Furthermore, an improved stability criterion and the true value of the basic reproduction number is presented. Abid Ali Lashari Copyright © 2015 Abid Ali Lashari. All rights reserved. Text Mining for Translational Bioinformatics Wed, 26 Aug 2015 11:57:15 +0000 Hong-Jie Dai, Chih-Hsuan Wei, Hung-Yu Kao, Rey-Long Liu, Richard Tzong-Han Tsai, and Zhiyong Lu Copyright © 2015 Hong-Jie Dai et al. All rights reserved. Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields Wed, 26 Aug 2015 07:11:52 +0000 Electronic health record (EHR) is a digital data format that collects electronic health information about an individual patient or population. To enhance the meaningful use of EHRs, information extraction techniques have been developed to recognize clinical concepts mentioned in EHRs. Nevertheless, the clinical judgment of an EHR cannot be known solely based on the recognized concepts without considering its contextual information. In order to improve the readability and accessibility of EHRs, this work developed a section heading recognition system for clinical documents. In contrast to formulating the section heading recognition task as a sentence classification problem, this work proposed a token-based formulation with the conditional random field (CRF) model. A standard section heading recognition corpus was compiled by annotators with clinical experience to evaluate the performance and compare it with sentence classification and dictionary-based approaches. The results of the experiments showed that the proposed method achieved a satisfactory F-score of 0.942, which outperformed the sentence-based approach and the best dictionary-based system by 0.087 and 0.096, respectively. One important advantage of our formulation over the sentence-based approach is that it presented an integrated solution without the need to develop additional heuristics rules for isolating the headings from the surrounding section contents. Hong-Jie Dai, Shabbir Syed-Abdul, Chih-Wei Chen, and Chieh-Chen Wu Copyright © 2015 Hong-Jie Dai et al. All rights reserved. Disease Related Knowledge Summarization Based on Deep Graph Search Tue, 25 Aug 2015 08:21:27 +0000 The volume of published biomedical literature on disease related knowledge is expanding rapidly. Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher’s information needs. In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information. Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities. Finally random walk algorithm is exploited to filter out the weak relations. The experimental results show that our approach achieves a precision of 60% and a recall of 61% on salient information extraction for Carcinoma of bladder and outperforms the method of Combo. Xiaofang Wu, Zhihao Yang, ZhiHeng Li, Hongfei Lin, and Jian Wang Copyright © 2015 Xiaofang Wu et al. All rights reserved. Supervised Learning Based Hypothesis Generation from Biomedical Literature Tue, 25 Aug 2015 08:06:19 +0000 Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system. Shengtian Sang, Zhihao Yang, Zongyao Li, and Hongfei Lin Copyright © 2015 Shengtian Sang et al. All rights reserved. GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains Tue, 25 Aug 2015 07:11:22 +0000 The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator. Chih-Hsuan Wei, Hung-Yu Kao, and Zhiyong Lu Copyright © 2015 Chih-Hsuan Wei et al. All rights reserved. Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records Tue, 25 Aug 2015 06:34:01 +0000 Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302. Jitendra Jonnagaddala, Siaw-Teng Liaw, Pradeep Ray, Manish Kumar, Hong-Jie Dai, and Chien-Yeh Hsu Copyright © 2015 Jitendra Jonnagaddala et al. All rights reserved. MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies Tue, 25 Aug 2015 06:28:47 +0000 The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe “MetaRNA-Seq,” a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level. Pankaj Kumar, Anna Halama, Shahina Hayat, Anja M. Billing, Manish Gupta, Noha A. Yousri, Gregory M. Smith, and Karsten Suhre Copyright © 2015 Pankaj Kumar et al. All rights reserved. Activation of Apoptotic Signal in Endothelial Cells through Intracellular Signaling Molecules Blockade in Tumor-Induced Angiogenesis Tue, 04 Aug 2015 13:04:48 +0000 Tumor-induced angiogenesis is the bridge between avascular and vascular tumor growth phases. In tumor-induced angiogenesis, endothelial cells start to migrate and proliferate toward the tumor and build new capillaries toward the tumor. There are two stages for sprout extension during angiogenesis. The first stage is prior to anastomosis, when single sprouts extend. The second stage is after anastomosis when closed flow pathways or loops are formed and blood flows in the closed loops. Prior to anastomosis, biochemical and biomechanical signals from extracellular matrix regulate endothelial cell phenotype; however, after anastomosis, blood flow is the main regulator of endothelial cell phenotype. In this study, the critical signaling pathways of each stage are introduced. A Boolean network model is used to map environmental and flow induced signals to endothelial cell phenotype (proliferation, migration, apoptosis, and lumen formation). Using the Boolean network model, blockade of intracellular signaling molecules of endothelial cell is investigated prior to and after anastomosis and the cell fate is obtained in each case. Activation of apoptotic signal in endothelial cell can prevent the extension of new vessels and may inhibit angiogenesis. It is shown that blockade of a few signaling molecules in endothelial cell activates apoptotic signal that are proposed as antiangiogenic strategies. Hossein Bazmara, M. Soltani, Kaamran Raahemifar, Mostafa Sefidgar, Majid Bazargan, Mojtaba Mousavi Naeenian, and Ali Elkamel Copyright © 2015 Hossein Bazmara et al. All rights reserved. Sequence and Structure Analysis of Biological Molecules Based on Computational Methods Sun, 05 Jul 2015 09:51:33 +0000 Jia-Feng Yu, Yue-Dong Yang, Xiao Sun, and Ji-Hua Wang Copyright © 2015 Jia-Feng Yu et al. All rights reserved. An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions Sun, 05 Jul 2015 09:49:43 +0000 SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions. Junliang Shang, Yan Sun, Shengjun Li, Jin-Xing Liu, Chun-Hou Zheng, and Junying Zhang Copyright © 2015 Junliang Shang et al. All rights reserved. Labeling of Chromosomes in Cell Development and the Appearance of Monozygotic Twins Sun, 21 Jun 2015 07:19:22 +0000 Understanding the mechanism behind the structure of the internal cellular clock can lead to advances in the knowledge of origins of pairs of monozygotic twins and higher order multiples as well as other biological phenomena. To gain insight into this mechanism, we analyze possible cell labeling schemes that model an organism’s development. Our findings lead us to predict that monozygotic quadruplets are not quadruplets in the traditional sense but rather two pairs of monozygotic twins where the pairs slightly differ—a situation we coin quadruplet twins. From the considered model, the probability of monozygotic twins is found to be , and we discover that the probability of monozygotic quadruplets, or triplets as in the case of the death of an embryo, is , where K is a species-specific integer representing the number of pairs of homologous chromosomes. The parameter K may determine cancerization with a probability threshold that is approximately inversely proportional to the Hayflick limit. Exposure to some cancerization factors such as small levels of ionizing radiation and chemical pollution may not produce cancer. Carol Jim and Simon Berkovich Copyright © 2015 Carol Jim and Simon Berkovich. All rights reserved. Evolutionary and Expression Analysis of miR-#-5p and miR-#-3p at the miRNAs/isomiRs Levels Tue, 05 May 2015 11:18:31 +0000 We mainly discussed miR-#-5p and miR-#-3p under three aspects: (1) primary evolutionary analysis of human miRNAs; (2) evolutionary analysis of miRNAs from different arms across the typical 10 vertebrates; (3) expression pattern analysis of miRNAs at the miRNA/isomiR levels using public small RNA sequencing datasets. We found that no bias can be detected between the numbers of 5p-miRNA and 3p-miRNA, while miRNAs from miR-#-5p and miR-#-3p show variable nucleotide compositions. IsomiR expression profiles from the two arms are always stable, but isomiR expressions in diseased samples are prone to show larger degree of dispersion. miR-#-5p and miR-#-3p have relative independent evolution/expression patterns and datasets of target mRNAs, which might also contribute to the phenomena of arm selection and/or arm switching. Simultaneously, miRNA/isomiR expression profiles may be regulated via arm selection and/or arm switching, and the dynamic miRNAome and isomiRome will adapt to functional and/or evolutionary pressures. A comprehensive analysis and further experimental study at the miRNA/isomiR levels are quite necessary for miRNA study. Li Guo, Jiafeng Yu, Hao Yu, Yang Zhao, Shujie Chen, Changqing Xu, and Feng Chen Copyright © 2015 Li Guo et al. All rights reserved. Constraint Programming Based Biomarker Optimization Tue, 05 May 2015 08:47:06 +0000 Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling. Manli Zhou, Youxi Luo, Guoquan Sun, Guoqin Mai, and Fengfeng Zhou Copyright © 2015 Manli Zhou et al. All rights reserved. Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens Tue, 05 May 2015 06:33:28 +0000 Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer from weak-label problem; thus protein function prediction with incomplete annotation matches well with the MIML with weak-label learning framework. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation. Jian-Sheng Wu, Hai-Feng Hu, Shan-Cheng Yan, and Li-Hua Tang Copyright © 2015 Jian-Sheng Wu et al. All rights reserved. Predicting Homogeneous Pilus Structure from Monomeric Data and Sparse Constraints Mon, 04 May 2015 12:58:18 +0000 Type IV pili (T4P) and T2SS (Type II Secretion System) pseudopili are filaments extending beyond microbial surfaces, comprising homologous subunits called “pilins.” In this paper, we presented a new approach to predict pseudo atomic models of pili combining ambiguous symmetric constraints with sparse distance information obtained from experiments and based neither on electronic microscope (EM) maps nor on accurate a priori symmetric details. The approach was validated by the reconstruction of the gonococcal (GC) pilus from Neisseria gonorrhoeae, the type IVb toxin-coregulated pilus (TCP) from Vibrio cholerae, and pseudopilus of the pullulanase T2SS (the PulG pilus) from Klebsiella oxytoca. In addition, analyses of computational errors showed that subunits should be treated cautiously, as they are slightly flexible and not strictly rigid bodies. A global sampling in a wider range was also implemented and implied that a pilus might have more than one but fewer than many possible intact conformations. Ke Xiao, Chuanjun Shu, Qin Yan, and Xiao Sun Copyright © 2015 Ke Xiao et al. All rights reserved. Nucleosome Organization around Pseudogenes in the Human Genome Mon, 04 May 2015 12:51:51 +0000 Pseudogene, disabled copy of functional gene, plays a subtle role in gene expression and genome evolution. The first step in deciphering RNA-level regulation of pseudogenes is to understand their transcriptional activity. So far, there has been no report on possible roles of nucleosome organization in pseudogene transcription. In this paper, we investigated the effect of nucleosome positioning on pseudogene transcription. For transcribed pseudogenes, the experimental nucleosome occupancy shows a prominent depletion at the regions both upstream of pseudogene start positions and downstream of pseudogene end positions. Intriguingly, the same depletion is also observed for nontranscribed pseudogenes, which is unexpected since nucleosome depletion in those regions is thought to be unnecessary in light of the nontranscriptional property of those pseudogenes. The sequence-dependent prediction of nucleosome occupancy shows a consistent pattern with the experimental data-based analysis. Our results indicate that nucleosome positioning may play important roles in both the transcription initiation and termination of pseudogenes. Guoqing Liu, Fen Feng, Xiujuan Zhao, and Lu Cai Copyright © 2015 Guoqing Liu et al. All rights reserved. A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction Mon, 04 May 2015 12:50:17 +0000 Nicotine, as the major psychoactive component of tobacco, has broad physiological effects within the central nervous system, but our understanding of the molecular mechanism underlying its neuronal effects remains incomplete. In this study, we performed a systematic analysis on a set of nicotine addiction-related genes to explore their characteristics at network levels. We found that NAGenes tended to have a more moderate degree and weaker clustering coefficient and to be less central in the network compared to alcohol addiction-related genes or cancer genes. Further, clustering of these genes resulted in six clusters with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis, which provided an intuitional view on the major molecular functions of the genes. Moreover, functional enrichment analysis revealed that neurodevelopment, neurotransmission activity, and metabolism related biological processes were involved in nicotine addiction. In summary, by analyzing the overall characteristics of the nicotine addiction related genes, this study provided valuable information for understanding the molecular mechanisms underlying nicotine addiction. Meng Liu, Xia Li, Rui Fan, Xinhua Liu, and Ju Wang Copyright © 2015 Meng Liu et al. All rights reserved. Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites Mon, 04 May 2015 12:23:54 +0000 Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable . Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where “nonspecific” interactions contribute to biological function. Lorraine Marsh Copyright © 2015 Lorraine Marsh. All rights reserved. Redesigning Protein Cavities as a Strategy for Increasing Affinity in Protein-Protein Interaction: Interferon-γ Receptor 1 as a Model Tue, 28 Apr 2015 06:58:13 +0000 Combining computational and experimental tools, we present a new strategy for designing high affinity variants of a binding protein. The affinity is increased by mutating residues not at the interface, but at positions lining internal cavities of one of the interacting molecules. Filling the cavities lowers flexibility of the binding protein, possibly reducing entropic penalty of binding. The approach was tested using the interferon-γ receptor 1 (IFNγR1) complex with IFNγ as a model. Mutations were selected from 52 amino acid positions lining the IFNγR1 internal cavities by using a protocol based on FoldX prediction of free energy changes. The final four mutations filling the IFNγR1 cavities and potentially improving the affinity to IFNγ were expressed, purified, and refolded, and their affinity towards IFNγ was measured by SPR. While individual cavity mutations yielded receptor constructs exhibiting only slight increase of affinity compared to WT, combinations of these mutations with previously characterized variant N96W led to a significant sevenfold increase. The affinity increase in the high affinity receptor variant N96W+V35L is linked to the restriction of its molecular fluctuations in the unbound state. The results demonstrate that mutating cavity residues is a viable strategy for designing protein variants with increased affinity. Jiří Černý, Lada Biedermannová, Pavel Mikulecký, Jiří Zahradník, Tatsiana Charnavets, Peter Šebo, and Bohdan Schneider Copyright © 2015 Jiří Černý et al. All rights reserved. Detecting Protein-Protein Interactions with a Novel Matrix-Based Protein Sequence Representation and Support Vector Machines Mon, 27 Apr 2015 11:33:03 +0000 Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally. Zhu-Hong You, Jianqiang Li, Xin Gao, Zhou He, Lin Zhu, Ying-Ke Lei, and Zhiwei Ji Copyright © 2015 Zhu-Hong You et al. All rights reserved. An Improved Method for Completely Uncertain Biological Network Alignment Mon, 27 Apr 2015 11:13:51 +0000 With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of current approaches, an improved method called completely probabilistic biological network comparison alignment (C_PBNA) is proposed in this paper. This new method is designed for complete probabilistic biological network alignment based on probabilistic biological network alignment (PBNA) in order to take full advantage of the uncertain information of biological network. The degree of consistency (agreement) indicates that C_PBNA can find the results neglected by PBNA algorithm. Furthermore, the GO consistency (GOC) and global network alignment score (GNAS) have been selected as evaluation criteria, and all of them proved that C_PBNA can obtain more biologically significant results than those of PBNA algorithm. Bin Shen, Muwei Zhao, Wei Zhong, and Jieyue He Copyright © 2015 Bin Shen et al. All rights reserved. Mathematical Modeling of Subthreshold Resonant Properties in Pyloric Dilator Neurons Thu, 16 Apr 2015 07:43:53 +0000 Various types of neurons exhibit subthreshold resonance oscillation (preferred frequency response) to fluctuating sinusoidal input currents. This phenomenon is well known to influence the synaptic plasticity and frequency of neural network oscillation. This study evaluates the resonant properties of pacemaker pyloric dilator (PD) neurons in the central pattern generator network through mathematical modeling. From the pharmacological point of view, calcium currents cannot be blocked in PD neurons without removing the calcium-dependent potassium current. Thus, the effects of calcium and calcium-dependent potassium currents on resonant properties remain unclear. By taking advantage of Hodgkin-Huxley-type model of neuron and its equivalent RLC circuit, we examine the effects of changing resting membrane potential and those ionic currents on the resonance. Results show that changing the resting membrane potential influences the amplitude and frequency of resonance so that the strength of resonance (Q-value) increases by both depolarization and hyperpolarization of the resting membrane potential. Moreover, hyperpolarization-activated inward current and (in association with ) are dominant factors on resonant properties at hyperpolarized and depolarized potentials, respectively. Through mathematical analysis, results indicate that and affect the resonant properties of PD neurons. However, only has an amplifying effect on the resonance amplitude of these neurons. Babak Vazifehkhah Ghaffari, Mojgan Kouhnavard, Takeshi Aihara, and Tatsuo Kitajima Copyright © 2015 Babak Vazifehkhah Ghaffari et al. All rights reserved. Recovering Drug-Induced Apoptosis Subnetwork from Connectivity Map Data Wed, 25 Mar 2015 12:09:14 +0000 The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. Since the therapeutic goal of most anticancer drugs is to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using CMAP microarray data from breast cancer cell line MCF7, we applied a Gaussian Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular-pathway. We then focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network. In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in the literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway. Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as “gateway” genes in the drug-induced intrinsic and extrinsic apoptosis pathways. Jiyang Yu, Preeti Putcha, and Jose M. Silva Copyright © 2015 Jiyang Yu et al. All rights reserved. A Time-Series Model of Phase Amplitude Cross Frequency Coupling and Comparison of Spectral Characteristics with Neural Data Thu, 19 Mar 2015 13:42:23 +0000 Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposed models, based on multiplying a pulsatile function of a low-frequency oscillation () with an unobserved and high-frequency component, yields a process with a spectrum that is consistent with observation. Other models, such as those employing a biphasic pulsatile function of a low-frequency oscillation, are demonstrated to be less suitable. We introduce the full stochastic process time series model as a summation of three component weak-sense stationary (WSS) processes, namely, , , and , with a noise process. The process is constructed as a product of a latent and unobserved high-frequency process with a function of the lagged, low-frequency oscillatory component (). After demonstrating that the model process is WSS, an appropriate method of simulation is introduced based upon the WSS property. This work may be of interest to researchers seeking to connect inhibitory and excitatory dynamics directly to observation in a model that accounts for known temporal dependence or to researchers seeking to examine what can occur in a multiplicative time-domain CFC mechanism. Kyle Q. Lepage and Sujith Vijayan Copyright © 2015 Kyle Q. Lepage and Sujith Vijayan. All rights reserved. Computational Selection of RNA Aptamer against Angiopoietin-2 and Experimental Evaluation Thu, 19 Mar 2015 07:55:42 +0000 Angiogenesis plays a decisive role in the growth and spread of cancer and angiopoietin-2 (Ang2) is in the spotlight of studies for its unique role in modulating angiogenesis. The aim of this study was to introduce a computational simulation approach to screen aptamers with high binding ability for Ang2. We carried out computational simulations of aptamer-protein interactions by using ZDOCK and ZRANK functions in Discovery Studio 3.5 starting from the available information of aptamers generated through the systematic evolution of ligands by exponential enrichment (SELEX) in the literature. From the best of three aptamers on the basis of ZRANK scores, 189 sequences with two-point mutations were created and simulated with Ang2. Then, we used a surface plasmon resonance (SPR) biosensor to test 3 mutant sequences of high ZRANK scores along with a high and a low affinity binding sequence as reported in the literature. We found a selected RNA aptamer has a higher binding affinity and SPR response than a reported sequence with the highest affinity. This is the first study of in silico selection of aptamers against Ang2 by using the ZRANK scoring function, which should help to increase the efficiency of selecting aptamers with high target-binding ability. Wen-Pin Hu, Jangam Vikram Kumar, Chun-Jen Huang, and Wen-Yih Chen Copyright © 2015 Wen-Pin Hu et al. All rights reserved. Discover the Molecular Biomarker Associated with Cell Death and Extracellular Matrix Module in Ovarian Cancer Mon, 16 Mar 2015 12:37:28 +0000 High throughput technologies have provided many new research methods for ovarian cancer investigation. In tradition, in order to find the underlying functional mechanisms of the survival-associated genes, gene sets enrichment analysis (GSEA) is always regarded as the important choice. However, GSEA produces too many candidate genes and cannot discover the signaling transduction cascades. In this work, we have used a network-based strategy to optimize the discovery of biomarkers using multifactorial data, including patient expression, clinical survival, and protein-protein interaction (PPI) data. The biomarkers discovered by this strategy belong to the network-based biomarker, which is apt to reveal the underlying functional mechanisms of the biomarker. In this work, over 400 expression arrays in ovarian cancer have been analyzed: the results showed that cell death and extracellular module are the main themes related to ovarian cancer progression. Qiang Liu, Jianxin Guo, Jinghong Cui, Jing Wang, and Ping Yi Copyright © 2015 Qiang Liu et al. All rights reserved. Application of Stochastic Automata Networks for Creation of Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels Sun, 01 Feb 2015 14:06:32 +0000 The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC) of voltage gating of gap junction (GJ) channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of the stochastic automata networks (SANs), which allowed for very efficient building and storing of infinitesimal generator of the CTMC that allowed to produce matrices of the models containing a distinct block structure. All of that allowed us to develop efficient numerical methods for a steady-state solution of CTMC models. This allowed us to accelerate CPU time, which is necessary to solve CTMC models, ∼20 times. Mindaugas Snipas, Henrikas Pranevicius, Mindaugas Pranevicius, Osvaldas Pranevicius, Nerijus Paulauskas, and Feliksas F. Bukauskas Copyright © 2015 Mindaugas Snipas et al. All rights reserved. MIC as an Appropriate Method to Construct the Brain Functional Network Sun, 01 Feb 2015 12:01:01 +0000 Using an effective method to measure the brain functional connectivity is an important step to study the brain functional network. The main methods for constructing an undirected brain functional network include correlation coefficient (CF), partial correlation coefficient (PCF), mutual information (MI), wavelet correlation coefficient (WCF), and coherence (CH). In this paper we demonstrate that the maximal information coefficient (MIC) proposed by Reshef et al. is relevant to constructing a brain functional network because it performs best in the comprehensive comparisons in consistency and robustness. Our work can be used to validate the possible new functional connection measures. Ziqing Zhang, Shu Sun, Ming Yi, Xia Wu, and Yiming Ding Copyright © 2015 Ziqing Zhang et al. All rights reserved. Proposal for a New Noncontact Method for Measuring Tongue Moisture to Assist in Tongue Diagnosis and Development of the Tongue Image Analyzing System, Which Can Separately Record the Gloss Components of the Tongue Wed, 28 Jan 2015 12:35:07 +0000 Tongue diagnosis is a noninvasive diagnosis and is traditionally one of the most important tools for physicians who practice Kampo (traditional Japanese) medicine. However, it is a subjective process, and its results can depend on the experience of the physician performing it. Previous studies have reported how to measure and evaluate the shape and color of the tongue objectively. Therefore, this study focused on the glossy component in order to quantify tongue moisture in tongue diagnosis. We hypothesized that moisture appears as a gloss in captured images and measured the amount of water on the tongue surface in 13 subjects. The results showed a high correlation between the degree of gloss and the amount of water on the tongue surface and suggested that the moisture on the tongue can be estimated by the degree of gloss in a captured image. Because the moisture level on the tongue changes during the course of taking photos, it became clear that we had to wait at least 3 minutes between photos. Based on these results, we established the tongue image analyzing system (TIAS), which can consistently record the gloss and color of the tongue surface simultaneously. Toshiya Nakaguchi, Kanako Takeda, Yuya Ishikawa, Takeshi Oji, Satoshi Yamamoto, Norimichi Tsumura, Keigo Ueda, Koichi Nagamine, Takao Namiki, and Yoichi Miyake Copyright © 2015 Toshiya Nakaguchi et al. All rights reserved. Computational Study of Correlated Domain Motions in the AcrB Efflux Transporter Mon, 05 Jan 2015 06:57:53 +0000 As active part of the major efflux system in E. coli bacteria, AcrB is responsible for the uptake and pumping of toxic substrates from the periplasm toward the extracellular space. In combination with the channel protein TolC and membrane fusion protein AcrA, this efflux pump is able to help the bacterium to survive different kinds of noxious compounds. With the present study we intend to enhance the understanding of the interactions between the domains and monomers, for example, the transduction of mechanical energy from the transmembrane domain into the porter domain, correlated motions of different subdomains within monomers, and cooperative effects between monomers. To this end, targeted molecular dynamics simulations have been employed either steering the whole protein complex or specific parts thereof. By forcing only parts of the complex towards specific conformational states, the risk for transient artificial conformations during the simulations is reduced. Distinct cooperative effects between the monomers in AcrB have been observed. Possible allosteric couplings have been identified providing microscopic insights that might be exploited to design more efficient inhibitors of efflux systems. Robert Schulz, Attilio V. Vargiu, Paolo Ruggerone, and Ulrich Kleinekathöfer Copyright © 2015 Robert Schulz et al. All rights reserved. Computational and Bioinformatics Techniques for Immunology Wed, 31 Dec 2014 15:25:02 +0000 Francesco Pappalardo, Vladimir Brusic, Filippo Castiglione, and Christian Schönbach Copyright © 2014 Francesco Pappalardo et al. All rights reserved. High-Performance Computing and Big Data in Omics-Based Medicine Mon, 22 Dec 2014 12:05:26 +0000 Ivan Merelli, Horacio Pérez-Sánchez, Sandra Gesing, and Daniele D’Agostino Copyright © 2014 Ivan Merelli et al. All rights reserved. Development of a Finite Element Head Model for the Study of Impact Head Injury Wed, 22 Oct 2014 10:49:26 +0000 This study is aimed at developing a high quality, validated finite element (FE) human head model for traumatic brain injuries (TBI) prediction and prevention during vehicle collisions. The geometry of the FE model was based on computed tomography (CT) and magnetic resonance imaging (MRI) scans of a volunteer close to the anthropometry of a 50th percentile male. The material and structural properties were selected based on a synthesis of current knowledge of the constitutive models for each tissue. The cerebrospinal fluid (CSF) was simulated explicitly as a hydrostatic fluid by using a surface-based fluid modeling method. The model was validated in the loading condition observed in frontal impact vehicle collision. These validations include the intracranial pressure (ICP), brain motion, impact force and intracranial acceleration response, maximum von Mises stress in the brain, and maximum principal stress in the skull. Overall results obtained in the validation indicated improved biofidelity relative to previous FE models, and the change in the maximum von Mises in the brain is mainly caused by the improvement of the CSF simulation. The model may be used for improving the current injury criteria of the brain and anthropometric test devices. Bin Yang, Kwong-Ming Tse, Ning Chen, Long-Bin Tan, Qing-Qian Zheng, Hui-Min Yang, Min Hu, Gang Pan, and Heow-Pueh Lee Copyright © 2014 Bin Yang et al. All rights reserved. Erratum to “A Bioinformatics Pipeline for the Analyses of Viral Escape Dynamics and Host Immune Responses during an Infection” Sun, 21 Sep 2014 06:02:49 +0000 Preston Leung, Rowena Bull, Andrew Lloyd, and Fabio Luciani Copyright © 2014 Preston Leung et al. All rights reserved. Differential Protein Network Analysis of the Immune Cell Lineage Sun, 21 Sep 2014 00:00:00 +0000 Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks. Trevor Clancy and Eivind Hovig Copyright © 2014 Trevor Clancy and Eivind Hovig. All rights reserved. Modelling the Formation of Liver Zones within the Scope of Fractional Order Derivative Mon, 08 Sep 2014 07:21:57 +0000 We develop and extend earlier results related to mathematical modelling of the liver formation zone by the adoption of noninteger order derivative. The hidden uncertainties in the model are captured and controlled thanks to the Caputo derivative. The stationary states are investigated and the time-dependent solution is approximated using two recent iteration methods. In particular, we discuss the convergence of these methods by constructing a suitable Hilbert space. Abdon Atangana and Suares Clovis Oukouomi Noutchie Copyright © 2014 Abdon Atangana and Suares Clovis Oukouomi Noutchie. All rights reserved. Managing, Analysing, and Integrating Big Data in Medical Bioinformatics: Open Problems and Future Perspectives Mon, 01 Sep 2014 05:41:44 +0000 The explosion of the data both in the biomedical research and in the healthcare systems demands urgent solutions. In particular, the research in omics sciences is moving from a hypothesis-driven to a data-driven approach. Healthcare is additionally always asking for a tighter integration with biomedical data in order to promote personalized medicine and to provide better treatments. Efficient analysis and interpretation of Big Data opens new avenues to explore molecular biology, new questions to ask about physiological and pathological states, and new ways to answer these open issues. Such analyses lead to better understanding of diseases and development of better and personalized diagnostics and therapeutics. However, such progresses are directly related to the availability of new solutions to deal with this huge amount of information. New paradigms are needed to store and access data, for its annotation and integration and finally for inferring knowledge and making it available to researchers. Bioinformatics can be viewed as the “glue” for all these processes. A clear awareness of present high performance computing (HPC) solutions in bioinformatics, Big Data analysis paradigms for computational biology, and the issues that are still open in the biomedical and healthcare fields represent the starting point to win this challenge. Ivan Merelli, Horacio Pérez-Sánchez, Sandra Gesing, and Daniele D’Agostino Copyright © 2014 Ivan Merelli et al. All rights reserved. System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks Wed, 27 Aug 2014 12:42:10 +0000 Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM. Mingzhi Liao, Yingbo Miao, Liangcai Zhang, Yang Wang, Rennan Feng, Lei Yang, Shihua Zhang, Yongshuai Jiang, and Guiyou Liu Copyright © 2014 Mingzhi Liao et al. All rights reserved. Microsoft Kinect-Based Artificial Perception System for Control of Functional Electrical Stimulation Assisted Grasping Tue, 19 Aug 2014 05:17:02 +0000 We present a computer vision algorithm that incorporates a heuristic model which mimics a biological control system for the estimation of control signals used in functional electrical stimulation (FES) assisted grasping. The developed processing software acquires the data from Microsoft Kinect camera and implements real-time hand tracking and object analysis. This information can be used to identify temporal synchrony and spatial synergies modalities for FES control. Therefore, the algorithm acts as artificial perception which mimics human visual perception by identifying the position and shape of the object with respect to the position of the hand in real time during the planning phase of the grasp. This artificial perception used within the heuristically developed model allows selection of the appropriate grasp and prehension. The experiments demonstrate that correct grasp modality was selected in more than 90% of tested scenarios/objects. The system is portable, and the components are low in cost and robust; hence, it can be used for the FES in clinical or even home environment. The main application of the system is envisioned for functional electrical therapy, that is, intensive exercise assisted with FES. Matija Štrbac, Slobodan Kočović, Marko Marković, and Dejan B. Popović Copyright © 2014 Matija Štrbac et al. All rights reserved. An Improved Approach for Accurate and Efficient Measurement of Common Carotid Artery Intima-Media Thickness in Ultrasound Images Mon, 18 Aug 2014 09:01:53 +0000 The intima-media thickness (IMT) of common carotid artery (CCA) can serve as an important indicator for the assessment of cardiovascular diseases (CVDs). In this paper an improved approach for automatic IMT measurement with low complexity and high accuracy is presented. 100 ultrasound images from 100 patients were tested with the proposed approach. The ground truth (GT) of the IMT was manually measured for six times and averaged, while the automatic segmented (AS) IMT was computed by the algorithm proposed in this paper. The mean difference ± standard deviation between AS and GT IMT is 0.0231 ± 0.0348 mm, and the correlation coefficient between them is 0.9629. The computational time is 0.3223 s per image with MATLAB under Windows XP on an Intel Core 2 Duo CPU E7500 @2.93 GHz. The proposed algorithm has the potential to achieve real-time measurement under Visual Studio. Qiang Li, Wei Zhang, Xin Guan, Yu Bai, and Jing Jia Copyright © 2014 Qiang Li et al. All rights reserved. Alzheimer’s Disease and HLA-A2: Linking Neurodegenerative to Immune Processes through an In Silico Approach Sun, 17 Aug 2014 11:10:34 +0000 There is a controversial relationship between HLA-A2 and Alzheimer’s disease (AD). It has been suggested a modifier effect on the risk that depends on genetic loadings. Thus, the aims of this study were to evaluate this relationship and to reveal genes associated with both concepts the HLA-A gene and AD. Consequently, we did first a classical systematic review and a meta-analysis of case-control studies. Next, by means of an in silico approach, we used experimental knowledge of protein-protein interactions to evaluate the top ranked genes shared by both concepts, previously found through text mining. The meta-analysis did not show a significant pooled OR (1.11, 95% CI: 0.98 to 1.24 in Caucasians), in spite of the fact that four of the included studies had a significant OR > 1 and none of them a significant OR < 1. In contrast, the in silico approach retrieved nonrandomly shared genes by both concepts (P = 0.02), which additionally encode truly interacting proteins. The network of proteins encoded by APP, ICAM-1, ITGB2, ITGAL, SELP, SELL, IL2, IL1B, CD4, and CD8A linked immune to neurodegenerative processes and highlighted the potential roles in AD pathogenesis of endothelial regulation, infectious diseases, specific antigen presentation, and HLA-A2 in maintaining synapses. Ricardo A. Cifuentes and Juan Murillo-Rojas Copyright © 2014 Ricardo A. Cifuentes and Juan Murillo-Rojas. All rights reserved. Polyglot Programming in Applications Used for Genetic Data Analysis Thu, 14 Aug 2014 06:44:36 +0000 Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development. Robert M. Nowak Copyright © 2014 Robert M. Nowak. All rights reserved. A Novel Mutation in the Transglutaminase-1 Gene in an Autosomal Recessive Congenital Ichthyosis Patient Sun, 10 Aug 2014 13:09:24 +0000 Structure-function implication on a novel homozygous Trp250/Gly mutation of transglutaminase-1 (TGM1) observed in a patient of autosomal recessive congenital ichthyosis is invoked from a bioinformatics analysis. Structural consequences of this mutation are hypothesized in comparison to homologous enzyme human factor XIIIA accepted as valid in similar structural analysis and are projected as guidelines for future studies at an experimental level on TGM1 thus mutated. D. Vaigundan, Neha V. Kalmankar, J. Krishnappa, N. Yellappa Gowda, A. V. M. Kutty, and Patnam R. Krishnaswamy Copyright © 2014 D. Vaigundan et al. All rights reserved. Biomedical Data Integration, Modeling, and Simulation in the Era of Big Data and Translational Medicine Thu, 24 Jul 2014 11:01:30 +0000 Bairong Shen, Andrew E. Teschendorff, Degui Zhi, and Junfeng Xia Copyright © 2014 Bairong Shen et al. All rights reserved. 1,3,4-Oxadiazole Derivatives: Synthesis, Characterization, Antimicrobial Potential, and Computational Studies Thu, 24 Jul 2014 09:52:50 +0000 We report the synthesis and biological assessment of 1,3,4-oxadiazole substituted 24 derivatives as novel, potential antibacterial agents. The structures of the newly synthesized derivatives were established by the combined practice of UV, IR, 1H NMR, 13C NMR, and mass spectrometry. Further these synthesized derivatives were subjected to antibacterial activity against all the selected microbial strains in comparison with amoxicillin and cefixime. The antibacterial activity of synthesized derivatives was correlated with their physicochemical and structural properties by QSAR analysis using computer assisted multiple regression analysis and four sound predictive models were generated with good , , and Fischer statistic. The derivatives with potent antibacterial activity were subjected to molecular docking studies to investigate the interactions between the active derivatives and amino acid residues existing in the active site of peptide deformylase to assess their antibacterial potential as peptide deformylase inhibitor. Suman Bala, Sunil Kamboj, Anu Kajal, Vipin Saini, and Deo Nanadan Prasad Copyright © 2014 Suman Bala et al. All rights reserved. Sequence Alignment Tools: One Parallel Pattern to Rule Them All? Thu, 24 Jul 2014 09:22:56 +0000 In this paper, we advocate high-level programming methodology for next generation sequencers (NGS) alignment tools for both productivity and absolute performance. We analyse the problem of parallel alignment and review the parallelisation strategies of the most popular alignment tools, which can all be abstracted to a single parallel paradigm. We compare these tools to their porting onto the FastFlow pattern-based programming framework, which provides programmers with high-level parallel patterns. By using a high-level approach, programmers are liberated from all complex aspects of parallel programming, such as synchronisation protocols, and task scheduling, gaining more possibility for seamless performance tuning. In this work, we show some use cases in which, by using a high-level approach for parallelising NGS tools, it is possible to obtain comparable or even better absolute performance for all used datasets. Claudia Misale, Giulio Ferrero, Massimo Torquati, and Marco Aldinucci Copyright © 2014 Claudia Misale et al. All rights reserved. A Hadoop-Based Method to Predict Potential Effective Drug Combination Wed, 23 Jul 2014 10:01:51 +0000 Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request. Yifan Sun, Yi Xiong, Qian Xu, and Dongqing Wei Copyright © 2014 Yifan Sun et al. All rights reserved. On the Coupling of Two Models of the Human Immune Response to an Antigen Tue, 22 Jul 2014 10:59:52 +0000 The development of mathematical models of the immune response allows a better understanding of the multifaceted mechanisms of the defense system. The main purpose of this work is to present a scheme for coupling distinct models of different scales and aspects of the immune system. As an example, we propose a new model where the local tissue inflammation processes are simulated with partial differential equations (PDEs) whereas a system of ordinary differential equations (ODEs) is used as a model for the systemic response. The simulation of distinct scenarios allows the analysis of the dynamics of various immune cells in the presence of an antigen. Preliminary results of this approach with a sensitivity analysis of the coupled model are shown but further validation is still required. Bárbara de M. Quintela, Rodrigo Weber dos Santos, and Marcelo Lobosco Copyright © 2014 Bárbara de M. Quintela et al. All rights reserved. Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information Mon, 21 Jul 2014 00:00:00 +0000 The recognition of protein folds is an important step for the prediction of protein structure and function. After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered. Therefore, the recognition of 27-class protein folds with the interaction of segments and motif information is very important. Based on the 27-class folds dataset built by Liu et al., amino acid composition, the interactions of secondary structure segments, motif frequency, and predicted secondary structure information were extracted. Using the Random Forest algorithm and the ensemble classification strategy, 27-class protein folds and corresponding structural classification were identified by independent test. The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively. When the training set and testing set were combined, the overall accuracy by 5-fold cross validation was 81.16%. In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak’s dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained. Zhenxing Feng and Xiuzhen Hu Copyright © 2014 Zhenxing Feng and Xiuzhen Hu. All rights reserved. Modeling Biology Spanning Different Scales: An Open Challenge Thu, 17 Jul 2014 12:02:59 +0000 It is coming nowadays more clear that in order to obtain a unified description of the different mechanisms governing the behavior and causality relations among the various parts of a living system, the development of comprehensive computational and mathematical models at different space and time scales is required. This is one of the most formidable challenges of modern biology characterized by the availability of huge amount of high throughput measurements. In this paper we draw attention to the importance of multiscale modeling in the framework of studies of biological systems in general and of the immune system in particular. Filippo Castiglione, Francesco Pappalardo, Carlo Bianca, Giulia Russo, and Santo Motta Copyright © 2014 Filippo Castiglione et al. All rights reserved. Analysis of EEG Signals Related to Artists and Nonartists during Visual Perception, Mental Imagery, and Rest Using Approximate Entropy Tue, 15 Jul 2014 11:07:37 +0000 In this paper, differences between multichannel EEG signals of artists and nonartists were analyzed during visual perception and mental imagery of some paintings and at resting condition using approximate entropy (ApEn). It was found that ApEn is significantly higher for artists during the visual perception and the mental imagery in the frontal lobe, suggesting that artists process more information during these conditions. It was also observed that ApEn decreases for the two groups during the visual perception due to increasing mental load; however, their variation patterns are different. This difference may be used for measuring progress in novice artists. In addition, it was found that ApEn is significantly lower during the visual perception than the mental imagery in some of the channels, suggesting that visual perception task requires more cerebral efforts. Nasrin Shourie, Mohammad Firoozabadi, and Kambiz Badie Copyright © 2014 Nasrin Shourie et al. All rights reserved. Polymorphisms at Amino Acid Residues 141 and 154 Influence Conformational Variation in Ovine PrP Mon, 14 Jul 2014 13:27:26 +0000 Polymorphisms in ovine PrP at amino acid residues 141 and 154 are associated with susceptibility to ovine prion disease: Leu141Arg154 with classical scrapie and Phe141Arg154 and Leu141His154 with atypical scrapie. Classical scrapie is naturally transmissible between sheep, whereas this may not be the case with atypical scrapie. Critical amino acid residues will determine the range or stability of structural changes within the ovine prion protein or its functional interaction with potential cofactors, during conversion of PrPC to PrPSc in these different forms of scrapie disease. Here we computationally identified that regions of ovine PrP, including those near amino acid residues 141 and 154, displayed more conservation than expected based on local structural environment. Molecular dynamics simulations showed these conserved regions of ovine PrP displayed genotypic differences in conformational repertoire and amino acid side-chain interactions. Significantly, Leu141Arg154 PrP adopted an extended beta sheet arrangement in the N-terminal palindromic region more frequently than the Phe141Arg154 and Leu141His154 variants. We supported these computational observations experimentally using circular dichroism spectroscopy and immunobiochemical studies on ovine recombinant PrP. Collectively, our observations show amino acid residues 141 and 154 influence secondary structure and conformational change in ovine PrP that may correlate with different forms of scrapie. Sujeong Yang, Alana M. Thackray, Lee Hopkins, Tom P. Monie, David F. Burke, and Raymond Bujdoso Copyright © 2014 Sujeong Yang et al. All rights reserved. Biomechanical Analysis of Force Distribution in Human Finger Extensor Mechanisms Wed, 09 Jul 2014 00:00:00 +0000 The complexities of the function and structure of human fingers have long been recognised. The in vivo forces in the human finger tendon network during different activities are critical information for clinical diagnosis, surgical treatment, prosthetic finger design, and biomimetic hand development. In this study, we propose a novel method for in vivo force estimation for the finger tendon network by combining a three-dimensional motion analysis technique and a novel biomechanical tendon network model. The extensor mechanism of a human index finger is represented by an interconnected tendinous network moving around the phalanx’s dorsum. A novel analytical approach based on the “Principle of Minimum Total Potential Energy” is used to calculate the forces and deformations throughout the tendon network of the extensor mechanism when subjected to an external load and with the finger posture defined by measurement data. The predicted deformations and forces in the tendon network are in broad agreement with the results obtained by previous experimental in vitro studies. The proposed methodology provides a promising tool for investigating the biomechanical function of complex interconnected tendon networks in vivo. Dan Hu, Lei Ren, David Howard, and Changfu Zong Copyright © 2014 Dan Hu et al. All rights reserved. Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment Tue, 08 Jul 2014 00:00:00 +0000 The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue () wound images grabbed by normal digital camera were first transformed into (hue, saturation, and intensity) color space and subsequently the “” component of color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793). Rashmi Mukherjee, Dhiraj Dhane Manohar, Dev Kumar Das, Arun Achar, Analava Mitra, and Chandan Chakraborty Copyright © 2014 Rashmi Mukherjee et al. All rights reserved. Dual Binding Site and Selective Acetylcholinesterase Inhibitors Derived from Integrated Pharmacophore Models and Sequential Virtual Screening Wed, 25 Jun 2014 00:00:00 +0000 In this study, we have employed in silico methodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer’s randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties. Shikhar Gupta and C. Gopi Mohan Copyright © 2014 Shikhar Gupta and C. Gopi Mohan. All rights reserved. Morphometric Evaluation of Preeclamptic Placenta Using Light Microscopic Images Mon, 23 Jun 2014 12:01:26 +0000 Deficient trophoblast invasion and anomalies in placental development generally lead to preeclampsia (PE) but the inter-relationship between placental function and morphology in PE still remains unknown. The aim of this study was to evaluate the morphometric features of placental villi and capillaries in preeclamptic and normal placentae. The study included light microscopic images of placental tissue sections of 40 preeclamptic and 35 normotensive pregnant women. Preprocessing and segmentation of these images were performed to characterize the villi and capillaries. Fisher’s linear discriminant analysis (FLDA), hierarchical cluster analysis (HCA), and principal component analysis (PCA) were applied to identify the most significant placental (morphometric) features from microscopic images. A total of 10 morphometric features were extracted, of which the villous parameters were significantly altered in PE. FLDA identified 5 highly significant morphometric features (>90% overall discrimination accuracy). Two large subclusters were clearly visible in HCA based dendrogram. PCA returned three most significant principal components cumulatively explaining 98.4% of the total variance based on these 5 significant features. Hence, quantitative microscopic evaluation revealed that placental morphometry plays an important role in characterizing PE, where the villous is the major component that is affected. Rashmi Mukherjee Copyright © 2014 Rashmi Mukherjee. All rights reserved. The Impact of Coinfections and Their Simultaneous Transmission on Antigenic Diversity and Epidemic Cycling of Infectious Diseases Sun, 22 Jun 2014 13:20:21 +0000 Epidemic cycling in human infectious diseases is common; however, its underlying mechanisms have been poorly understood. Much effort has been made to search for external mechanisms. Multiple strains of an infectious agent were usually observed and coinfections were frequent; further, empirical evidence indicates the simultaneous transmission of coinfections. To explore intrinsic mechanisms for epidemic cycling, in this study we consider a multistrain Susceptible-Infected-Recovered-Susceptible epidemic model by including coinfections and simultaneous transmission. We show that coinfections and their simultaneous transmission widen the parameter range for coexistence and coinfections become popular when strains enhance each other and the immunity wanes quickly. However, the total prevalence is nearly independent of these characteristics and approximated by that of one-strain model. With sufficient simultaneous transmission and antigenic diversity, cyclical epidemics can be generated even when strains interfere with each other by reducing infectivity. This indicates that strain interactions within coinfections and cross-immunity during subsequent infection provide a possible intrinsic mechanism for epidemic cycling. Xu-Sheng Zhang and Ke-Fei Cao Copyright © 2014 Xu-Sheng Zhang and Ke-Fei Cao. All rights reserved. On Designing Multicore-Aware Simulators for Systems Biology Endowed with OnLine Statistics Sun, 22 Jun 2014 08:18:02 +0000 The paper arguments are on enabling methodologies for the design of a fully parallel, online, interactive tool aiming to support the bioinformatics scientists .In particular, the features of these methodologies, supported by the FastFlow parallel programming framework, are shown on a simulation tool to perform the modeling, the tuning, and the sensitivity analysis of stochastic biological models. A stochastic simulation needs thousands of independent simulation trajectories turning into big data that should be analysed by statistic and data mining tools. In the considered approach the two stages are pipelined in such a way that the simulation stage streams out the partial results of all simulation trajectories to the analysis stage that immediately produces a partial result. The simulation-analysis workflow is validated for performance and effectiveness of the online analysis in capturing biological systems behavior on a multicore platform and representative proof-of-concept biological systems. The exploited methodologies include pattern-based parallel programming and data streaming that provide key features to the software designers such as performance portability and efficient in-memory (big) data management and movement. Two paradigmatic classes of biological systems exhibiting multistable and oscillatory behavior are used as a testbed. Marco Aldinucci, Cristina Calcagno, Mario Coppo, Ferruccio Damiani, Maurizio Drocco, Eva Sciacca, Salvatore Spinella, Massimo Torquati, and Angelo Troina Copyright © 2014 Marco Aldinucci et al. All rights reserved. Big Data Analytics in Immunology: A Knowledge-Based Approach Sun, 22 Jun 2014 00:00:00 +0000 With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow. Guang Lan Zhang, Jing Sun, Lou Chitkushev, and Vladimir Brusic Copyright © 2014 Guang Lan Zhang et al. All rights reserved. The Current Status of Usability Studies of Information Technologies in China: A Systematic Study Thu, 19 Jun 2014 11:01:40 +0000 Objectives. To systematically review and analyze the current status and characteristics of usability studies in China in the field of information technology in general and in the field of healthcare in particular. Methods. We performed a quantitative literature analysis in three major Chinese academic databases and one English language database using Chinese search terms equivalent to the concept of usability. Results. Six hundred forty-seven publications were selected for analysis. We found that in China the literature on usability in the field of information technology began in 1994 and increased thereafter. The usability definitions from ISO 9241-11:1998 and Nielsen (1993) have been widely recognized and cited. Authors who have published several publications are rare. Fourteen journals have a publishing rate over 1%. Only nine publications about HIT were identified. Discussions. China’s usability research started relatively late. There is a lack of organized research teams and dedicated usability journals. High-impact theoretical studies are scarce. On the application side, no original and systematic research frameworks have been developed. The understanding and definition of usability is not well synchronized with international norms. Besides, usability research in HIT is rare. Conclusions. More human and material resources need to be invested in China’s usability research, particularly in HIT. Jianbo Lei, Lufei Xu, Qun Meng, Jiajie Zhang, and Yang Gong Copyright © 2014 Jianbo Lei et al. All rights reserved. Metadynamics Simulation Study on the Conformational Transformation of HhaI Methyltransferase: An Induced-Fit Base-Flipping Hypothesis Thu, 19 Jun 2014 08:55:33 +0000 DNA methyltransferases play crucial roles in establishing and maintenance of DNA methylation, which is an important epigenetic mark. Flipping the target cytosine out of the DNA helical stack and into the active site of protein provides DNA methyltransferases with an opportunity to access and modify the genetic information hidden in DNA. To investigate the conversion process of base flipping in the HhaI methyltransferase (M.HhaI), we performed different molecular simulation approaches on M.HhaI-DNA-S-adenosylhomocysteine ternary complex. The results demonstrate that the nonspecific binding of DNA to M.HhaI is initially induced by electrostatic interactions. Differences in chemical environment between the major and minor grooves determine the orientation of DNA. Gln237 at the target recognition loop recognizes the GCGC base pair from the major groove side by hydrogen bonds. In addition, catalytic loop motion is a key factor during this process. Our study indicates that base flipping is likely to be an “induced-fit” process. This study provides a solid foundation for future studies on the discovery and development of mechanism-based DNA methyltransferases regulators. Lu Jin, Fei Ye, Dan Zhao, Shijie Chen, Kongkai Zhu, Mingyue Zheng, Ren-Wang Jiang, Hualiang Jiang, and Cheng Luo Copyright © 2014 Lu Jin et al. All rights reserved. Rank-Based miRNA Signatures for Early Cancer Detection Wed, 18 Jun 2014 06:54:17 +0000 We describe a new signature definition and analysis method to be used as biomarker for early cancer detection. Our new approach is based on the construction of a reference map of transcriptional signatures of both healthy and cancer affected individuals using circulating miRNA from a large number of subjects. Once such a map is available, the diagnosis for a new patient can be performed by observing the relative position on the map of his/her transcriptional signature. To demonstrate its efficacy for this specific application we report the results of the application of our method to published datasets of circulating miRNA, and we quantify its performance compared to current state-of-the-art methods. A number of additional features make this method an ideal candidate for large-scale use, for example, as a mass screening tool for early cancer detection or for at-home diagnostics. Specifically, our method is minimally invasive (because it works well with circulating miRNA), it is robust with respect to lab-to-lab protocol variability and batch effects (it requires that only the relative ranking of expression value of miRNA in a profile be accurate not their absolute values), and it is scalable to a large number of subjects. Finally we discuss the need for HPC capability in a widespread application of our or similar methods. Mario Lauria Copyright © 2014 Mario Lauria. All rights reserved. Performance Studies on Distributed Virtual Screening Tue, 17 Jun 2014 08:01:41 +0000 Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly. Jens Krüger, Richard Grunzke, Sonja Herres-Pawlis, Alexander Hoffmann, Luis de la Garza, Oliver Kohlbacher, Wolfgang E. Nagel, and Sandra Gesing Copyright © 2014 Jens Krüger et al. All rights reserved. Geometric Analysis of Alloreactive HLA α-Helices Tue, 17 Jun 2014 05:43:46 +0000 Molecular dynamics (MD) is a valuable tool for the investigation of functional elements in biomolecules, providing information on dynamic properties and processes. Previous work by our group has characterized static geometric properties of the two MHC -helices comprising the peptide binding region recognized by T cells. We build upon this work and used several spline models to approximate the overall shape of MHC -helices. We applied this technique to a series of MD simulations of alloreactive MHC molecules that allowed us to capture the dynamics of MHC -helices’ steric configurations. Here, we discuss the variability of spline models underlying the geometric analysis with varying polynomial degrees of the splines. Reiner Ribarics, Rudolf Karch, Nevena Ilieva, and Wolfgang Schreiner Copyright © 2014 Reiner Ribarics et al. All rights reserved. Massive Exploration of Perturbed Conditions of the Blood Coagulation Cascade through GPU Parallelization Mon, 16 Jun 2014 00:00:00 +0000 The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations. Paolo Cazzaniga, Marco S. Nobile, Daniela Besozzi, Matteo Bellini, and Giancarlo Mauri Copyright © 2014 Paolo Cazzaniga et al. All rights reserved.