BioMed Research International: Computational Biology The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . 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. Phylogenetic Analysis of H7N9 Avian Influenza Virus Based on a Novel Mathematical Descriptor Mon, 16 Jun 2014 00:00:00 +0000 A new mathematical descriptor was proposed based on 3D graphical representation. Using the method, we construct the phylogenetic trees of nine proteins of H7N9 influenza virus to analyze the originated source of H7N9. The results show that the evolution route of H7N9 avian influenza is from America through Europe to Asia. Furthermore, two samples collected from environment in Nanjing and Zhejiang and one sample collected from chicken are the sources of H7N9 influenza virus that infected human in China. Yusheng Bai, Tingting Ma, Yuhua Yao, Qi Dai, and Ping-an He Copyright © 2014 Yusheng Bai et al. All rights reserved. A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing Sun, 15 Jun 2014 08:52:46 +0000 Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor. Ginés D. Guerrero, Baldomero Imbernón, Horacio Pérez-Sánchez, Francisco Sanz, José M. García, and José M. Cecilia Copyright © 2014 Ginés D. Guerrero et al. All rights reserved. Heart Health Risk Assessment System: A Nonintrusive Proposal Using Ontologies and Expert Rules Sun, 15 Jun 2014 06:51:18 +0000 According to the World Health Organization, the world’s leading cause of death is heart disease, with nearly two million deaths per year. Although some factors are not possible to change, there are some keys that help to prevent heart diseases. One of the most important keys is to keep an active daily life, with moderate exercise. However, deciding what a moderate exercise is or when a slightly abnormal heart rate value is a risk depends on the person and the activity. In this paper we propose a context-aware system that is able to determine the activity the person is performing in an unobtrusive way. Then, we have defined ontology to represent the available knowledge about the person (biometric data, fitness status, medical information, etc.) and her current activity (level of intensity, heart rate recommended for that activity, etc.). With such knowledge, a set of expert rules based on this ontology are involved in a reasoning process to infer levels of alerts or suggestions for the users when the intensity of the activity is detected as dangerous for her health. We show how this approach can be accomplished by using only everyday devices such as a smartphone and a smartwatch. Teresa Garcia-Valverde, Andrés Muñoz, Francisco Arcas, Andrés Bueno-Crespo, and Alberto Caballero Copyright © 2014 Teresa Garcia-Valverde et al. All rights reserved. Chaotic Analysis of the Electroretinographic Signal for Diagnosis Sun, 15 Jun 2014 00:00:00 +0000 Electroretinogram (ERG) is a time-varying potential which arises from different layers of retina. To be specific, all the physiological signals may contain some useful information which is not visible to our naked eye. However this subtle information is difficult to monitor directly. Therefore the ERG signal features which are extracted and analyzed using computers are highly useful for diagnosis. This work discusses the chaotic aspect of the ERG signal for the controls, congenital stationary night blindness (CSNB), and cone-rod dystrophy (CRD) classes. In this work, nonlinear parameters like Hurst exponent (HE), the largest Lyapunov exponent (LLE), Higuchi’s fractal dimension (HFD), and approximate entropy (ApEn) are analyzed for the three different classes. It is found that the measures like HE dimension and ApEn are higher for controls as compared to the other two classes. But LLE shows no distinguishable variation for the three cases. We have also analyzed the recurrence plots and phase-space plots which shows a drastic variation among the three groups. The results obtained show that the ERG signal is highly complex for the control groups and less complex for the abnormal classes with value less than 0.05. Surya S. Nair and K. Paul Joseph Copyright © 2014 Surya S. Nair and K. Paul Joseph. All rights reserved. A Combined MPI-CUDA Parallel Solution of Linear and Nonlinear Poisson-Boltzmann Equation Thu, 12 Jun 2014 10:54:45 +0000 The Poisson-Boltzmann equation models the electrostatic potential generated by fixed charges on a polarizable solute immersed in an ionic solution. This approach is often used in computational structural biology to estimate the electrostatic energetic component of the assembly of molecular biological systems. In the last decades, the amount of data concerning proteins and other biological macromolecules has remarkably increased. To fruitfully exploit these data, a huge computational power is needed as well as software tools capable of exploiting it. It is therefore necessary to move towards high performance computing and to develop proper parallel implementations of already existing and of novel algorithms. Nowadays, workstations can provide an amazing computational power: up to 10 TFLOPS on a single machine equipped with multiple CPUs and accelerators such as Intel Xeon Phi or GPU devices. The actual obstacle to the full exploitation of modern heterogeneous resources is efficient parallel coding and porting of software on such architectures. In this paper, we propose the implementation of a full Poisson-Boltzmann solver based on a finite-difference scheme using different and combined parallel schemes and in particular a mixed MPI-CUDA implementation. Results show great speedups when using the two schemes, achieving an 18.9x speedup using three GPUs. José Colmenares, Antonella Galizia, Jesús Ortiz, Andrea Clematis, and Walter Rocchia Copyright © 2014 José Colmenares et al. All rights reserved. Data Mining in Translational Bioinformatics Thu, 12 Jun 2014 08:20:39 +0000 Xing-Ming Zhao, Jean X. Gao, and Jose C. Nacher Copyright © 2014 Xing-Ming Zhao et al. All rights reserved. An Improved Distance Matrix Computation Algorithm for Multicore Clusters Thu, 12 Jun 2014 06:41:16 +0000 Distance matrix has diverse usage in different research areas. Its computation is typically an essential task in most bioinformatics applications, especially in multiple sequence alignment. The gigantic explosion of biological sequence databases leads to an urgent need for accelerating these computations. DistVect algorithm was introduced in the paper of Al-Neama et al. (in press) to present a recent approach for vectorizing distance matrix computing. It showed an efficient performance in both sequential and parallel computing. However, the multicore cluster systems, which are available now, with their scalability and performance/cost ratio, meet the need for more powerful and efficient performance. This paper proposes DistVect1 as highly efficient parallel vectorized algorithm with high performance for computing distance matrix, addressed to multicore clusters. It reformulates DistVect1 vectorized algorithm in terms of clusters primitives. It deduces an efficient approach of partitioning and scheduling computations, convenient to this type of architecture. Implementations employ potential of both MPI and OpenMP libraries. Experimental results show that the proposed method performs improvement of around 3-fold speedup upon SSE2. Further it also achieves speedups more than 9 orders of magnitude compared to the publicly available parallel implementation utilized in ClustalW-MPI. Mohammed W. Al-Neama, Naglaa M. Reda, and Fayed F. M. Ghaleb Copyright © 2014 Mohammed W. Al-Neama et al. All rights reserved. Parallel Solutions for Voxel-Based Simulations of Reaction-Diffusion Systems Thu, 12 Jun 2014 06:12:13 +0000 There is an increasing awareness of the pivotal role of noise in biochemical processes and of the effect of molecular crowding on the dynamics of biochemical systems. This necessity has given rise to a strong need for suitable and sophisticated algorithms for the simulation of biological phenomena taking into account both spatial effects and noise. However, the high computational effort characterizing simulation approaches, coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviours, makes such kind of algorithms very time-consuming for studying real systems. So far, different parallelization approaches have been deployed to reduce the computational time required to simulate the temporal dynamics of biochemical systems using stochastic algorithms. In this work we discuss these aspects for the spatial TAU-leaping in crowded compartments (STAUCC) simulator, a voxel-based method for the stochastic simulation of reaction-diffusion processes which relies on the S-DPP algorithm. In particular we present how the characteristics of the algorithm can be exploited for an effective parallelization on the present heterogeneous HPC architectures. Daniele D’Agostino, Giulia Pasquale, Andrea Clematis, Carlo Maj, Ettore Mosca, Luciano Milanesi, and Ivan Merelli Copyright © 2014 Daniele D’Agostino et al. All rights reserved. Privacy Preserving RBF Kernel Support Vector Machine Thu, 12 Jun 2014 00:00:00 +0000 Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data. Haoran Li, Li Xiong, Lucila Ohno-Machado, and Xiaoqian Jiang Copyright © 2014 Haoran Li et al. All rights reserved. A Mathematical Model of Skeletal Muscle Disease and Immune Response in the mdx Mouse Wed, 11 Jun 2014 15:31:31 +0000 Duchenne muscular dystrophy (DMD) is a genetic disease that results in the death of affected boys by early adulthood. The genetic defect responsible for DMD has been known for over 25 years, yet at present there is neither cure nor effective treatment for DMD. During early disease onset, the mdx mouse has been validated as an animal model for DMD and use of this model has led to valuable but incomplete insights into the disease process. For example, immune cells are thought to be responsible for a significant portion of muscle cell death in the mdx mouse; however, the role and time course of the immune response in the dystrophic process have not been well described. In this paper we constructed a simple mathematical model to investigate the role of the immune response in muscle degeneration and subsequent regeneration in the mdx mouse model of Duchenne muscular dystrophy. Our model suggests that the immune response contributes substantially to the muscle degeneration and regeneration processes. Furthermore, the analysis of the model predicts that the immune system response oscillates throughout the life of the mice, and the damaged fibers are never completely cleared. Abdul Salam Jarrah, Filippo Castiglione, Nicholas P. Evans, Robert W. Grange, and Reinhard Laubenbacher Copyright © 2014 Abdul Salam Jarrah et al. All rights reserved. OpenMebius: An Open Source Software for Isotopically Nonstationary 13C-Based Metabolic Flux Analysis Wed, 11 Jun 2014 11:56:29 +0000 The in vivo measurement of metabolic flux by 13C-based metabolic flux analysis (13C-MFA) provides valuable information regarding cell physiology. Bioinformatics tools have been developed to estimate metabolic flux distributions from the results of tracer isotopic labeling experiments using a 13C-labeled carbon source. Metabolic flux is determined by nonlinear fitting of a metabolic model to the isotopic labeling enrichment of intracellular metabolites measured by mass spectrometry. Whereas 13C-MFA is conventionally performed under isotopically constant conditions, isotopically nonstationary 13C metabolic flux analysis (INST-13C-MFA) has recently been developed for flux analysis of cells with photosynthetic activity and cells at a quasi-steady metabolic state (e.g., primary cells or microorganisms under stationary phase). Here, the development of a novel open source software for INST-13C-MFA on the Windows platform is reported. OpenMebius (Open source software for Metabolic flux analysis) provides the function of autogenerating metabolic models for simulating isotopic labeling enrichment from a user-defined configuration worksheet. Analysis using simulated data demonstrated the applicability of OpenMebius for INST-13C-MFA. Confidence intervals determined by INST-13C-MFA were less than those determined by conventional methods, indicating the potential of INST-13C-MFA for precise metabolic flux analysis. OpenMebius is the open source software for the general application of INST-13C-MFA. Shuichi Kajihata, Chikara Furusawa, Fumio Matsuda, and Hiroshi Shimizu Copyright © 2014 Shuichi Kajihata et al. All rights reserved. A Bioinformatics Pipeline for the Analyses of Viral Escape Dynamics and Host Immune Responses during an Infection Tue, 10 Jun 2014 05:26:00 +0000 Rapidly mutating viruses, such as hepatitis C virus (HCV) and HIV, have adopted evolutionary strategies that allow escape from the host immune response via genomic mutations. Recent advances in high-throughput sequencing are reshaping the field of immuno-virology of viral infections, as these allow fast and cheap generation of genomic data. However, due to the large volumes of data generated, a thorough understanding of the biological and immunological significance of such information is often difficult. This paper proposes a pipeline that allows visualization and statistical analysis of viral mutations that are associated with immune escape. Taking next generation sequencing data from longitudinal analysis of HCV viral genomes during a single HCV infection, along with antigen specific T-cell responses detected from the same subject, we demonstrate the applicability of these tools in the context of primary HCV infection. We provide a statistical and visual explanation of the relationship between cooccurring mutations on the viral genome and the parallel adaptive immune response against HCV. Preston Leung, Rowena Bull, Andrew Lloyd, and Fabio Luciani Copyright © 2014 Preston Leung et al. All rights reserved. Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations Mon, 09 Jun 2014 08:51:43 +0000 In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data. Dong-Chul Kim, Jiao Wang, Chunyu Liu, and Jean Gao Copyright © 2014 Dong-Chul Kim et al. All rights reserved. A Cognitive Computational Model Inspired by the Immune System Response Mon, 09 Jun 2014 07:34:02 +0000 The immune system has a cognitive ability to differentiate between healthy and unhealthy cells. The immune system response (ISR) is stimulated by a disorder in the temporary fuzzy state that is oscillating between the healthy and unhealthy states. However, modeling the immune system is an enormous challenge; the paper introduces an extensive summary of how the immune system response functions, as an overview of a complex topic, to present the immune system as a cognitive intelligent agent. The homogeneity and perfection of the natural immune system have been always standing out as the sought-after model we attempted to imitate while building our proposed model of cognitive architecture. The paper divides the ISR into four logical phases: setting a computational architectural diagram for each phase, proceeding from functional perspectives (input, process, and output), and their consequences. The proposed architecture components are defined by matching biological operations with computational functions and hence with the framework of the paper. On the other hand, the architecture focuses on the interoperability of main theoretical immunological perspectives (classic, cognitive, and danger theory), as related to computer science terminologies. The paper presents a descriptive model of immune system, to figure out the nature of response, deemed to be intrinsic for building a hybrid computational model based on a cognitive intelligent agent perspective and inspired by the natural biology. To that end, this paper highlights the ISR phases as applied to a case study on hepatitis C virus, meanwhile illustrating our proposed architecture perspective. Mohamed Abdo Abd Al-Hady, Amr Ahmed Badr, and Mostafa Abd Al-Azim Mostafa Copyright © 2014 Mohamed Abdo Abd Al-Hady et al. All rights reserved. Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics Mon, 09 Jun 2014 00:00:00 +0000 While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. Anjani Ragothaman, Sairam Chowdary Boddu, Nayong Kim, Wei Feinstein, Michal Brylinski, Shantenu Jha, and Joohyun Kim Copyright © 2014 Anjani Ragothaman et al. All rights reserved. Mathematical Model of Neuronal Morphology: Prenatal Development of the Human Dentate Nucleus Thu, 05 Jun 2014 12:24:01 +0000 The aim of the study was to quantify the morphological changes of the human dentate nucleus during prenatal development using mathematical models that take into account main morphometric parameters. The camera lucida drawings of Golgi impregnated neurons taken from human fetuses of gestational ages ranging from 14 to 41 weeks were analyzed. Four morphometric parameters, the size of the neuron, the dendritic complexity, maximum dendritic density, and the position of maximum density, were obtained using the modified Scholl method and fractal analysis. Their increase during the entire prenatal development can be adequately fitted with a simple exponential. The three parameters describing the evolution of branching complexity of the dendritic arbor positively correlated with the increase of the size of neurons, but with different rate constants, showing that the complex development of the dendritic arbor is complete during the prenatal period. The findings of the present study are in accordance with previous crude qualitative data on prenatal development of the human dentate nucleus, but provide much greater amount of fine details. The mathematical model developed here provides a sound foundation enabling further studies on natal development or analyzing neurological disorders during prenatal development. Katarina Rajković, Goran Bačić, Dušan Ristanović, and Nebojša T. Milošević Copyright © 2014 Katarina Rajković et al. All rights reserved. Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets Mon, 02 Jun 2014 09:59:28 +0000 In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer. Fang Liu, Yaning Feng, Zhenye Li, Chao Pan, Yuncong Su, Rui Yang, Liying Song, Huilong Duan, and Ning Deng Copyright © 2014 Fang Liu et al. All rights reserved. Network Based Integrated Analysis of Phenotype-Genotype Data for Prioritization of Candidate Symptom Genes Mon, 02 Jun 2014 09:45:06 +0000 Background. Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms. Xing Li, Xuezhong Zhou, Yonghong Peng, Baoyan Liu, Runshun Zhang, Jingqing Hu, Jian Yu, Caiyan Jia, and Changkai Sun Copyright © 2014 Xing Li et al. All rights reserved. Identification of MicroRNAs as Potential Biomarker for Gastric Cancer by System Biological Analysis Wed, 28 May 2014 13:18:57 +0000 Gastric cancers (GC) have the high morbidity and mortality rates worldwide and there is a need to identify sufficiently sensitive biomarkers for GC. MicroRNAs (miRNAs) could be promising potential biomarkers for GC diagnosis. We employed a systematic and integrative bioinformatics framework to identify GC-related microRNAs from the public microRNA and mRNA expression dataset generated by RNA-seq technology. The performance of the 17 candidate miRNAs was evaluated by hierarchal clustering, ROC analysis, and literature mining. Fourteen have been found to be associated with GC and three microRNAs (miR-211, let-7b, and miR-708) were for the first time reported to associate with GC and may be used for diagnostic biomarkers for GC. Wenying Yan, Shouli Wang, Zhandong Sun, Yuxin Lin, Shengwei Sun, Jiajia Chen, and Weichang Chen Copyright © 2014 Wenying Yan et al. All rights reserved. Docking Applied to the Prediction of the Affinity of Compounds to P-Glycoprotein Tue, 27 May 2014 08:58:48 +0000 P-glycoprotein (P-gp) is involved in the transport of xenobiotic compounds and responsible for the decrease of the drug accumulation in multi-drug-resistant cells. In this investigation we compare several docking algorithms in order to find the conditions that are able to discriminate between P-gp binders and nonbinders. We built a comprehensive dataset of binders and nonbinders based on a careful analysis of the experimental data available in the literature, trying to overcome the discrepancy noticeable in the experimental results. We found that Autodock Vina flexible docking is the best choice for the tested options. The results will be useful to filter virtual screening results in the rational design of new drugs that are not expected to be expelled by P-gp. Pablo H. Palestro, Luciana Gavernet, Guillermina L. Estiu, and Luis E. Bruno Blanch Copyright © 2014 Pablo H. Palestro et al. All rights reserved. Simulated Annealing Based Algorithm for Identifying Mutated Driver Pathways in Cancer Mon, 26 May 2014 13:43:56 +0000 With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random “passenger mutations.” In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice. Hai-Tao Li, Yu-Lang Zhang, Chun-Hou Zheng, and Hong-Qiang Wang Copyright © 2014 Hai-Tao Li et al. All rights reserved. Qualitative and Quantitative Analysis for Facial Complexion in Traditional Chinese Medicine Thu, 22 May 2014 13:13:17 +0000 Facial diagnosis is an important and very intuitive diagnostic method in Traditional Chinese Medicine (TCM). However, due to its qualitative and experience-based subjective property, traditional facial diagnosis has a certain limitation in clinical medicine. The computerized inspection method provides classification models to recognize facial complexion (including color and gloss). However, the previous works only study the classification problems of facial complexion, which is considered as qualitative analysis in our perspective. For quantitative analysis expectation, the severity or degree of facial complexion has not been reported yet. This paper aims to make both qualitative and quantitative analysis for facial complexion. We propose a novel feature representation of facial complexion from the whole face of patients. The features are established with four chromaticity bases splitting up by luminance distribution on CIELAB color space. Chromaticity bases are constructed from facial dominant color using two-level clustering; the optimal luminance distribution is simply implemented with experimental comparisons. The features are proved to be more distinctive than the previous facial complexion feature representation. Complexion recognition proceeds by training an SVM classifier with the optimal model parameters. In addition, further improved features are more developed by the weighted fusion of five local regions. Extensive experimental results show that the proposed features achieve highest facial color recognition performance with a total accuracy of 86.89%. And, furthermore, the proposed recognition framework could analyze both color and gloss degrees of facial complexion by learning a ranking function. Changbo Zhao, Guo-zheng Li, Fufeng Li, Zhi Wang, and Chang Liu Copyright © 2014 Changbo Zhao et al. All rights reserved. The Analysis of the Disease Spectrum in China Thu, 22 May 2014 12:47:17 +0000 Analysis of the related risks of disease provides a scientific basis for disease prevention and treatment, hospital management, and policy formulation by the changes in disease spectrum of patients in hospital. Retrospective analysis was made to the first diagnosis, age, gender, daily average cost of hospitalized patients, and other factors in the First Affiliated Hospital of Nanjing Medical University during 2006–2013. The top 4 cases were as follows: cardiovascular disease, malignant tumors, lung infections, and noninsulin dependent diabetes mellitus. By the age of disease analysis, we found a younger age trend of cardiovascular disease, and the age of onset of cancer or diabetes was somewhat postponed. The average daily cost of hospitalization and the average daily cost of the main noncommunicable diseases were both on the rise. Noncommunicable diseases occupy an increasingly important position in the constitution of the disease, and they caused an increasing medical burden. People should pay attention to health from the aspects of lifestyle changing. Hospitals should focus on building the appropriate discipline. On the other hand, an integrated government response is required to tackle key risks. Multiple interventions are needed to lower the burden of these diseases and to improve national health. Xin Zhang, Xiaoping Zhou, Xinyi Huang, Shumei Miao, Hongwei Shan, Shenqi Jing, Tao Shan, Jianjun Guo, Jianqiu Kou, Zhongmin Wang, and Yun Liu Copyright © 2014 Xin Zhang et al. All rights reserved. Finding Semirigid Domains in Biomolecules by Clustering Pair-Distance Variations Thu, 15 May 2014 11:35:24 +0000 Dynamic variations in the distances between pairs of atoms are used for clustering subdomains of biomolecules. We draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed. This reduces the computational load of clustering drastically, and we demonstrate results for several biomolecules relevant in immunoinformatics. Results are evaluated regarding the number of clusters, cluster size, cluster stability, and the evolution of clusters over time. Crisp clustering lends itself as an efficient tool to locate semirigid domains in the simulation of biomolecules. Such domains seem crucial for an optimum performance of subsequent statistical analyses, aiming at detecting minute motional patterns related to antigen recognition and signal transduction. Michael Kenn, Reiner Ribarics, Nevena Ilieva, and Wolfgang Schreiner Copyright © 2014 Michael Kenn et al. All rights reserved. Computational Study to Determine When to Initiate and Alternate Therapy in HIV Infection Sun, 11 May 2014 14:24:44 +0000 HIV is a widespread viral infection without cure. Drug treatment has transformed HIV disease into a treatable long-term infection. However, the appearance of mutations within the viral genome reduces the susceptibility of HIV to drugs. Therefore, a key goal is to extend the time until patients exhibit resistance to all existing drugs. Current HIV treatment guidelines seem poorly supported as practitioners have not achieved a consensus on the optimal time to initiate and to switch antiretroviral treatments. We contribute to this discussion with predictions derived from a mathematical model of HIV dynamics. Our results indicate that early therapy initiation (within 2 years postinfection) is critical to delay AIDS progression. For patients who have not received any therapy during the first 3 years postinfection, switch in response to virological failure may outperform proactive switching strategies. In case that proactive switching is opted, the switching time between therapies should not be larger than 100 days. Further clinical trials are needed to either confirm or falsify these predictions. Matthias Haering, Andreas Hördt, Michael Meyer-Hermann, and Esteban A. Hernandez-Vargas Copyright © 2014 Matthias Haering et al. All rights reserved. Computational Prediction of Protein Function Based on Weighted Mapping of Domains and GO Terms Wed, 23 Apr 2014 13:45:28 +0000 In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains. Zhixia Teng, Maozu Guo, Qiguo Dai, Chunyu Wang, Jin Li, and Xiaoyan Liu Copyright © 2014 Zhixia Teng et al. All rights reserved. Agent-Based Modeling of the Immune System: NetLogo, a Promising Framework Tue, 22 Apr 2014 10:22:06 +0000 Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms. Ferdinando Chiacchio, Marzio Pennisi, Giulia Russo, Santo Motta, and Francesco Pappalardo Copyright © 2014 Ferdinando Chiacchio et al. All rights reserved. Pathway Bridge Based Multiobjective Optimization Approach for Lurking Pathway Prediction Wed, 16 Apr 2014 14:08:30 +0000 Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) is a protein with unknown function. Frequently methylated or downregulated, OCIAD2 has been observed in kinds of tumors, and TGFβ signaling has been proved to induce the expression of OCIAD2. However, current pathway analysis tools do not cover the genes without reported interactions like OCIAD2 and also miss some significant genes with relatively lower expression. To investigate potential biological milieu of OCIAD2, especially in cancer microenvironment, a nova approach pbMOO was created to find the potential pathways from TGFβ to OCIAD2 by searching on the pathway bridge, which consisted of cancer enriched looping patterns from the complicated entire protein interactions network. The pbMOO approach was further applied to study the modulator of ligand TGFβ1, receptor TGFβR1, intermediate transfer proteins, transcription factor, and signature OCIAD2. Verified by literature and public database, the pathway TGFβ1- TGFβR1- SMAD2/3- SMAD4/AR-OCIAD2 was detected, which concealed the androgen receptor (AR) which was the possible transcription factor of OCIAD2 in TGFβ signal, and it well explained the mechanism of TGFβ induced OCIAD2 expression in cancer microenvironment, therefore providing an important clue for the future functional analysis of OCIAD2 in tumor pathogenesis. Rengjing Zhang, Chen Zhao, Zixiang Xiong, and Xiaobo Zhou Copyright © 2014 Rengjing Zhang et al. All rights reserved. Gender-Specific DNA Methylome Analysis of a Han Chinese Longevity Population Mon, 14 Apr 2014 11:53:34 +0000 Human longevity is always a biological hotspot and so much effort has been devoted to identifying genes and genetic variations associated with longer lives. Most of the demographic studies have highlighted that females have a longer life span than males. The reasons for this are not entirely clear. In this study, we carried out a pool-based, epigenome-wide investigation of DNA methylation profiles in male and female nonagenarians/centenarians using the Illumina 450 K Methylation Beadchip assays. Although no significant difference was detected for the average methylation levels of examined CpGs (or probes) between male and female samples, a significant number of differentially methylated probes (DMPs) were identified, which appeared to be enriched in certain chromosome regions and certain parts of genes. Further analysis of DMP-containing genes (named DMGs) revealed that almost all of them are solely hypermethylated or hypomethylated. Functional enrichment analysis of these DMGs indicated that DNA hypermethylation and hypomethylation may regulate genes involved in different biological processes, such as hormone regulation, neuron projection, and disease-related pathways. This is the first effort to explore the gender-based methylome difference in nonagenarians/centenarians, which may provide new insights into the complex mechanism of longevity gender gap of human beings. Liang Sun, Jie Lin, Hongwu Du, Caiyou Hu, Zezhi Huang, Zeping Lv, Chenguang Zheng, Xiaohong Shi, Yan Zhang, and Ze Yang Copyright © 2014 Liang Sun et al. All rights reserved. Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis Mon, 07 Apr 2014 14:01:10 +0000 Skin biopsy images can reveal causes and severity of many skin diseases, which is a significant complement for skin surface inspection. Automatic annotation of skin biopsy image is an important problem for increasing efficiency and reducing the subjectiveness in diagnosis. However it is challenging particularly when there exists indirect relationship between annotation terms and local regions of a biopsy image, as well as local structures with different textures. In this paper, a novel method based on a recent proposed machine learning model, named multi-instance multilabel (MIML), is proposed to model the potential knowledge and experience of doctors on skin biopsy image annotation. We first show that the problem of skin biopsy image annotation can naturally be expressed as a MIML problem and then propose an image representation method that can capture both region structure and texture features, and a sparse Bayesian MIML algorithm which can produce probabilities indicating the confidence of annotation. The proposed algorithm framework is evaluated on a real clinical dataset containing 12,700 skin biopsy images. The results show that it is effective and prominent. Gang Zhang, Jian Yin, Xiangyang Su, Yongjing Huang, Yingrong Lao, Zhaohui Liang, Shanxing Ou, and Honglai Zhang Copyright © 2014 Gang Zhang et al. All rights reserved. Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis Sun, 06 Apr 2014 14:11:33 +0000 Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers. Jie Huang, Zhandong Sun, Wenying Yan, Yujie Zhu, Yuxin Lin, Jiajai Chen, Bairong Shen, and Jian Wang Copyright © 2014 Jie Huang et al. All rights reserved. In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches Sun, 06 Apr 2014 08:24:04 +0000 The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune responses, computer and mathematical models have been used in the last decades, enabling the representation of biological processes. In this modeling effort, a major issue is represented by the communication between models that work at cellular and molecular level, that is, multiscale representation. Here we sketch some attempts to model immune system dynamics at both levels. Mariagrazia Belfiore, Marzio Pennisi, Giuseppina Aricò, Simone Ronsisvalle, and Francesco Pappalardo Copyright © 2014 Mariagrazia Belfiore et al. All rights reserved. Identification of Simple Sequence Repeat Biomarkers through Cross-Species Comparison in a Tag Cloud Representation Mon, 31 Mar 2014 14:06:47 +0000 Simple sequence repeats (SSRs) are not only applied as genetic markers in evolutionary studies but they also play an important role in gene regulatory activities. Efficient identification of conserved and exclusive SSRs through cross-species comparison is helpful for understanding the evolutionary mechanisms and associations between specific gene groups and SSR motifs. In this paper, we developed an online cross-species comparative system and integrated it with a tag cloud visualization technique for identifying potential SSR biomarkers within fourteen frequently used model species. Ultraconserved or exclusive SSRs among cross-species orthologous genes could be effectively retrieved and displayed through a friendly interface design. Four different types of testing cases were applied to demonstrate and verify the retrieved SSR biomarker candidates. Through statistical analysis and enhanced tag cloud representation on defined functional related genes and cross-species clusters, the proposed system can correctly represent the patterns, loci, colors, and sizes of identified SSRs in accordance with gene functions, pattern qualities, and conserved characteristics among species. Jhen-Li Huang, Hao-Teng Chang, Ronshan Cheng, Hui-Huang Hsu, and Tun-Wen Pai Copyright © 2014 Jhen-Li Huang et al. All rights reserved. Global Analysis of miRNA Gene Clusters and Gene Families Reveals Dynamic and Coordinated Expression Tue, 25 Mar 2014 08:34:47 +0000 To further understand the potential expression relationships of miRNAs in miRNA gene clusters and gene families, a global analysis was performed in 4 paired tumor (breast cancer) and adjacent normal tissue samples using deep sequencing datasets. The compositions of miRNA gene clusters and families are not random, and clustered and homologous miRNAs may have close relationships with overlapped miRNA species. Members in the miRNA group always had various expression levels, and even some showed larger expression divergence. Despite the dynamic expression as well as individual difference, these miRNAs always indicated consistent or similar deregulation patterns. The consistent deregulation expression may contribute to dynamic and coordinated interaction between different miRNAs in regulatory network. Further, we found that those clustered or homologous miRNAs that were also identified as sense and antisense miRNAs showed larger expression divergence. miRNA gene clusters and families indicated important biological roles, and the specific distribution and expression further enrich and ensure the flexible and robust regulatory network. Li Guo, Sheng Yang, Yang Zhao, Hui Zhang, Qian Wu, and Feng Chen Copyright © 2014 Li Guo et al. All rights reserved. Evaluation and Comparison of Multiple Aligners for Next-Generation Sequencing Data Analysis Sun, 23 Mar 2014 06:48:44 +0000 Next-generation sequencing (NGS) technology has rapidly advanced and generated the massive data volumes. To align and map the NGS data, biologists often randomly select a number of aligners without concerning their suitable feature, high performance, and high accuracy as well as sequence variations and polymorphisms existing on reference genome. This study aims to systematically evaluate and compare the capability of multiple aligners for NGS data analysis. To explore this capability, we firstly performed alignment algorithms comparison and classification. We further used long-read and short-read datasets from both real-life and in silico NGS data for comparative analysis and evaluation of these aligners focusing on three criteria, namely, application-specific alignment feature, computational performance, and alignment accuracy. Our study demonstrated the overall evaluation and comparison of multiple aligners for NGS data analysis. This serves as an important guiding resource for biologists to gain further insight into suitable selection of aligners for specific and broad applications. Jing Shang, Fei Zhu, Wanwipa Vongsangnak, Yifei Tang, Wenyu Zhang, and Bairong Shen Copyright © 2014 Jing Shang et al. All rights reserved. Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm Mon, 17 Mar 2014 07:05:39 +0000 Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC. Zhiwei Ji and Bing Wang Copyright © 2014 Zhiwei Ji and Bing Wang. All rights reserved. A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics Thu, 06 Mar 2014 11:37:58 +0000 With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. Shan Li, Liying Kang, and Xing-Ming Zhao Copyright © 2014 Shan Li et al. All rights reserved. Data Analysis and Tissue Type Assignment for Glioblastoma Multiforme Mon, 03 Mar 2014 08:49:15 +0000 Glioblastoma multiforme (GBM) is characterized by high infiltration. The interpretation of MRSI data, especially for GBMs, is still challenging. Unsupervised methods based on NMF by Li et al. (2013, NMR in Biomedicine) and Li et al. (2013, IEEE Transactions on Biomedical Engineering) have been proposed for glioma recognition, but the tissue types is still not well interpreted. As an extension of the previous work, a tissue type assignment method is proposed for GBMs based on the analysis of MRSI data and tissue distribution information. The tissue type assignment method uses the values from the distribution maps of all three tissue types to interpret all the information in one new map and color encodes each voxel to indicate the tissue type. Experiments carried out on in vivo MRSI data show the feasibility of the proposed method. This method provides an efficient way for GBM tissue type assignment and helps to display information of MRSI data in a way that is easy to interpret. Yuqian Li, Yiming Pi, Xin Liu, Yuhan Liu, and Sofie Van Cauter Copyright © 2014 Yuqian Li et al. All rights reserved. Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation Tue, 11 Feb 2014 12:26:15 +0000 Accurate tumor classification is crucial to the proper treatment of cancer. To now, sparse representation (SR) has shown its great performance for tumor classification. This paper conceives a new SR-based method for tumor classification by using gene expression data. In the proposed method, we firstly use latent low-rank representation for extracting salient features and removing noise from the original samples data. Then we use sparse representation classifier (SRC) to build tumor classification model. The experimental results on several real-world data sets show that our method is more efficient and more effective than the previous classification methods including SVM, SRC, and LASSO. Bin Gan, Chun-Hou Zheng, Jun Zhang, and Hong-Qiang Wang Copyright © 2014 Bin Gan et al. All rights reserved. Evaluating the Influence of Motor Control on Selective Attention through a Stochastic Model: The Paradigm of Motor Control Dysfunction in Cerebellar Patient Sun, 09 Feb 2014 07:21:51 +0000 Attention allows us to selectively process the vast amount of information with which we are confronted, prioritizing some aspects of information and ignoring others by focusing on a certain location or aspect of the visual scene. Selective attention is guided by two cognitive mechanisms: saliency of the image (bottom up) and endogenous mechanisms (top down). These two mechanisms interact to direct attention and plan eye movements; then, the movement profile is sent to the motor system, which must constantly update the command needed to produce the desired eye movement. A new approach is described here to study how the eye motor control could influence this selection mechanism in clinical behavior: two groups of patients (SCA2 and late onset cerebellar ataxia LOCA) with well-known problems of motor control were studied; patients performed a cognitively demanding task; the results were compared to a stochastic model based on Monte Carlo simulations and a group of healthy subjects. The analytical procedure evaluated some energy functions for understanding the process. The implemented model suggested that patients performed an optimal visual search, reducing intrinsic noise sources. Our findings theorize a strict correlation between the “optimal motor system” and the “optimal stimulus encoders.” Giacomo Veneri, Antonio Federico, and Alessandra Rufa Copyright © 2014 Giacomo Veneri et al. All rights reserved. Detection of Epileptic Seizure Event and Onset Using EEG Wed, 29 Jan 2014 00:00:00 +0000 This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. Important features such as energy, entropy, standard deviation, maximum, minimum, and mean at different subbands were computed and classification was done using linear classifier. The performance of classifier was determined in terms of specificity, sensitivity, and accuracy. The overall accuracy was 84.2%. In the case of seizure onset detection, the database used is CHB-MIT scalp EEG database. Along with wavelet based features, interquartile range (IQR) and mean absolute deviation (MAD) without wavelet decomposition were extracted. Latency was used to study the performance of seizure onset detection. Classifier gave a sensitivity of 98.5% with an average latency of 1.76 seconds. Nabeel Ahammad, Thasneem Fathima, and Paul Joseph Copyright © 2014 Nabeel Ahammad et al. All rights reserved. 3D Assessment of Mandibular Growth Based on Image Registration: A Feasibility Study in a Rabbit Model Thu, 02 Jan 2014 11:29:35 +0000 Background. Our knowledge of mandibular growth mostly derives from cephalometric radiography, which has inherent limitations due to the two-dimensional (2D) nature of measurement. Objective. To assess 3D morphological changes occurring during growth in a rabbit mandible. Methods. Serial cone-beam computerised tomographic (CBCT) images were made of two New Zealand white rabbits, at baseline and eight weeks after surgical implantation of 1 mm diameter metallic spheres as fiducial markers. A third animal acted as an unoperated (no implant) control. CBCT images were segmented and registered in 3D (Implant Superimposition and Procrustes Method), and the remodelling pattern described used color maps. Registration accuracy was quantified by the maximal of the mean minimum distances and by the Hausdorff distance. Results. The mean error for image registration was 0.37 mm and never exceeded 1 mm. The implant-based superimposition showed most remodelling occurred at the mandibular ramus, with bone apposition posteriorly and vertical growth at the condyle. Conclusion. We propose a method to quantitatively describe bone remodelling in three dimensions, based on the use of bone implants as fiducial markers and CBCT as imaging modality. The method is feasible and represents a promising approach for experimental studies by comparing baseline growth patterns and testing the effects of growth-modification treatments. I. Kim, M. E. Oliveira, W. J. Duncan, I. Cioffi, and M. Farella Copyright © 2014 I. Kim et al. All rights reserved. Walking on a Tissue-Specific Disease-Protein-Complex Heterogeneous Network for the Discovery of Disease-Related Protein Complexes Sat, 28 Dec 2013 11:37:39 +0000 Besides the pinpointing of individual disease-related genes, associating protein complexes to human inherited diseases is also of great importance, because a biological function usually arises from the cooperative behaviour of multiple proteins in a protein complex. Moreover, knowledge about disease-related protein complexes could also enhance the inference of disease genes and pathogenic genetic variants. Here, we have designed a computational systems biology approach to systematically analyse potential relationships between diseases and protein complexes. First, we construct a heterogeneous network which is composed of a disease-disease similarity layer, a tissue-specific protein-protein interaction layer, and a protein complex membership layer. Then, we propose a random walk model on this disease-protein-complex network for identifying protein complexes that are related to a query disease. With a series of leave-one-out cross-validation experiments, we show that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We further predict a landscape of associations between human diseases and protein complexes. This landscape can be used to facilitate the inference of disease genes, thereby benefiting studies on pathology of diseases. Thibault Jacquemin and Rui Jiang Copyright © 2013 Thibault Jacquemin and Rui Jiang. All rights reserved. Improved scFv Anti-HIV-1 p17 Binding Affinity Guided from the Theoretical Calculation of Pairwise Decomposition Energies and Computational Alanine Scanning Thu, 07 Nov 2013 13:59:03 +0000 Computational approaches have been used to evaluate and define important residues for protein-protein interactions, especially antigen-antibody complexes. In our previous study, pairwise decomposition of residue interaction energies of single chain Fv with HIV-1 p17 epitope variants has indicated the key specific residues in the complementary determining regions (CDRs) of scFv anti-p17. In this present investigation in order to determine whether a specific side chain group of residue in CDRs plays an important role in bioactivity, computational alanine scanning has been applied. Molecular dynamics simulations were done with several complexes of original scFv anti-p17 and scFv anti-p17mutants with HIV-1 p17 epitope variants with a production run up to 10 ns. With the combination of pairwise decomposition residue interaction and alanine scanning calculations, the point mutation has been initially selected at the position MET100 to improve the residue binding affinity. The calculated docking interaction energy between a single mutation from methionine to either arginine or glycine has shown the improved binding affinity, contributed from the electrostatic interaction with the negative favorably interaction energy, compared to the wild type. Theoretical calculations agreed well with the results from the peptide ELISA results. Panthip Tue-ngeun, Kanchanok Kodchakorn, Piyarat Nimmanpipug, Narin Lawan, Sawitree Nangola, Chatchai Tayapiwatana, Noorsaadah Abdul Rahman, Sharifuddin Md. Zain, and Vannajan Sanghiran Lee Copyright © 2013 Panthip Tue-ngeun et al. All rights reserved. A Machine-Learned Predictor of Colonic Polyps Based on Urinary Metabolomics Thu, 07 Nov 2013 11:56:03 +0000 We report an automated diagnostic test that uses the NMR spectrum of a single spot urine sample to accurately distinguish patients who require a colonoscopy from those who do not. Moreover, our approach can be adjusted to tradeoff between sensitivity and specificity. We developed our system using a group of 988 patients (633 normal and 355 who required colonoscopy) who were all at average or above-average risk for developing colorectal cancer. We obtained a metabolic profile of each subject, based on the urine samples collected from these subjects, analyzed via 1H-NMR and quantified using targeted profiling. Each subject then underwent a colonoscopy, the gold standard to determine whether he/she actually had an adenomatous polyp, a precursor to colorectal cancer. The metabolic profiles, colonoscopy outcomes, and medical histories were then analysed using machine learning to create a classifier that could predict whether a future patient requires a colonoscopy. Our empirical studies show that this classifier has a sensitivity of 64% and a specificity of 65% and, unlike the current fecal tests, allows the administrators of the test to adjust the tradeoff between the two. Roman Eisner, Russell Greiner, Victor Tso, Haili Wang, and Richard N. Fedorak Copyright © 2013 Roman Eisner et al. All rights reserved. A Neural Network Model Can Explain Ventriloquism Aftereffect and Its Generalization across Sound Frequencies Mon, 21 Oct 2013 10:31:36 +0000 Exposure to synchronous but spatially disparate auditory and visual stimuli produces a perceptual shift of sound location towards the visual stimulus (ventriloquism effect). After adaptation to a ventriloquism situation, enduring sound shift is observed in the absence of the visual stimulus (ventriloquism aftereffect). Experimental studies report opposing results as to aftereffect generalization across sound frequencies varying from aftereffect being confined to the frequency used during adaptation to aftereffect generalizing across some octaves. Here, we present an extension of a model of visual-auditory interaction we previously developed. The new model is able to simulate the ventriloquism effect and, via Hebbian learning rules, the ventriloquism aftereffect and can be used to investigate aftereffect generalization across frequencies. The model includes auditory neurons coding both for the spatial and spectral features of the auditory stimuli and mimicking properties of biological auditory neurons. The model suggests that different extent of aftereffect generalization across frequencies can be obtained by changing the intensity of the auditory stimulus that induces different amounts of activation in the auditory layer. The model provides a coherent theoretical framework to explain the apparently contradictory results found in the literature. Model mechanisms and hypotheses are discussed in relation to neurophysiological and psychophysical data. Elisa Magosso, Filippo Cona, and Mauro Ursino Copyright © 2013 Elisa Magosso et al. All rights reserved. Modelling HIV/AIDS Epidemic among Men Who Have Sex with Men in China Mon, 30 Sep 2013 12:03:19 +0000 A compartmental model with antiviral therapy was proposed to identify the important factors that influence HIV infection among gay men in China and suggest some effective control strategies. We proved that the disease will be eradicated if the reproduction number is less than one. Based on the number of annual reported HIV/AIDS among MSM we used the Markov-Chain Monte-Carlo (MCMC) simulation to estimate the unknown parameters. We estimated a mean reproduction number of 3.88 (95% CI: 3.69–4.07). The estimation results showed that there were a higher transmission rate and a lower diagnose rate among MSM than those for another high-risk population. We compared the current treatment policy and immediate therapy once people are diagnosed with HIV, and numerical studies indicated that immediate antiviral therapy would lead to few HIV new infections conditional upon relatively low infectiousness; otherwise the current treatment policy would result in low HIV new infection. Further, increasing treatment coverage rate may lead to decline in HIV new infections and be beneficial to disease control, depending on the infectiousness of the infected individuals with antiviral therapy. The finding suggested that treatment efficacy (directly affecting infectiousness), behavior changes, and interventions greatly affect HIV new infection; strengthening intensity will contribute to the disease control. Xiaodan Sun, Yanni Xiao, Zhihang Peng, and Ning Wang Copyright © 2013 Xiaodan Sun et al. All rights reserved. Investigation of Nonlinear Pupil Dynamics by Recurrence Quantification Analysis Thu, 26 Sep 2013 15:15:17 +0000 Pupil is controlled by the autonomous nervous system (ANS). It shows complex movements and changes of size even in conditions of constant stimulation. The possibility of extracting information on ANS by processing data recorded during a short experiment using a low cost system for pupil investigation is studied. Moreover, the significance of nonlinear information contained in the pupillogram is investigated. We examined 13 healthy subjects in different stationary conditions, considering habitual dental occlusion (HDO) as a weak stimulation of the ANS with respect to the maintenance of the rest position (RP) of the jaw. Images of pupil captured by infrared cameras were processed to estimate position and size on each frame. From such time series, we extracted linear indexes (e.g., average size, average displacement, and spectral parameters) and nonlinear information using recurrence quantification analysis (RQA). Data were classified using multilayer perceptrons and support vector machines trained using different sets of input indexes: the best performance in classification was obtained including nonlinear indexes in the input features. These results indicate that RQA nonlinear indexes provide additional information on pupil dynamics with respect to linear descriptors, allowing the discrimination of even a slight stimulation of the ANS. Their use in the investigation of pathology is suggested. L. Mesin, A. Monaco, and R. Cattaneo Copyright © 2013 L. Mesin et al. All rights reserved. Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting Tue, 24 Sep 2013 11:16:09 +0000 Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%). Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings. Andrew D. Revell, Gerardo Alvarez-Uria, Dechao Wang, Anton Pozniak, Julio S. Montaner, H. Clifford Lane, and Brendan A. Larder Copyright © 2013 Andrew D. Revell et al. All rights reserved. Computational Analysis of the Soluble Form of the Intracellular Chloride Ion Channel Protein CLIC1 Sun, 08 Sep 2013 15:55:34 +0000 The chloride intracellular channel (CLIC) family of proteins has the remarkable property of maintaining both a soluble form and an integral membrane form acting as an ion channel. The soluble form is structurally related to the glutathione-S-transferase family, and CLIC can covalently bind glutathione via an active site cysteine. We report approximately 0.6 s of molecular dynamics simulations, encompassing the three possible ligand-bound states of CLIC1, using the structure of GSH-bound human CLIC1. Noncovalently bound GSH was rapidly released from the protein, whereas the covalently ligand-bound protein remained close to the starting structure over 0.25 s of simulation. In the unliganded state, conformational changes in the vicinity of the glutathione-binding site resulted in reduced reactivity of the active site thiol. Elastic network analysis indicated that the changes in the unliganded state are intrinsic to the protein architecture and likely represent functional transitions. Overall, our results are consistent with a model of CLIC function in which covalent binding of glutathione does not occur spontaneously but requires interaction with another protein to stabilise the GSH binding site and/or transfer of the ligand. The results do not indicate how CLIC1 undergoes a radical conformational change to form a transmembrane chloride channel but further elucidate the mechanism by which CLICs are redox controlled. Peter M. Jones, Paul M. G. Curmi, Stella M. Valenzuela, and Anthony M. George Copyright © 2013 Peter M. Jones et al. All rights reserved. Pharmacophore Modeling and Docking Studies on Some Nonpeptide-Based Caspase-3 Inhibitors Sun, 08 Sep 2013 15:40:34 +0000 Neurodegenerative disorders are major consequences of excessive apoptosis caused by a proteolytic enzyme known as caspase-3. Therefore, caspase-3 inhibition has become a validated therapeutic approach for neurodegenerative disorders. We performed pharmacophore modeling on some synthetic derivatives of caspase-3 inhibitors (pyrrolo[3,4-c]quinoline-1,3-diones) using PHASE 3.0. This resulted in the common pharmacophore hypothesis AAHRR.6 which might be responsible for the biological activity: two aromatic rings (R) mainly in the quinoline nucleus, one hydrophobic (H) group (CH3), and two acceptor (A) groups (–C=O). After identifying a valid hypothesis, we also developed an atom-based 3D-QSAR model applying the PLS algorithm. The developed model was statistically robust (; pred_). Additionally, we have performed molecular docking studies, cross-validated our results, and gained a deeper insight into its molecular recognition process. Our developed model may serve as a query tool for future virtual screening and drug designing for this particular target. Simant Sharma, Arijit Basu, and R. K. Agrawal Copyright © 2013 Simant Sharma et al. All rights reserved. A Novel Fuzzy Expert System for the Identification of Severity of Carpal Tunnel Syndrome Tue, 03 Sep 2013 14:16:38 +0000 The diagnosis of carpal tunnel syndrome, a peripheral nerve disorder, at the earliest possible stage is very crucial because if left untreated it may cause permanent nerve damage reducing the chances of successful treatment. Here a novel Fuzzy Expert System designed using MATLAB is proposed for identification of severity of CTS. The data used were the nerve conduction study data obtained from Kannur Medical College, India. It consists of thirteen input fields, which include the clinical values of the diagnostic test and the clinical symptoms, and the output field gives the disease severity. The results obtained match with the expert’s opinion with 98.4% accuracy and high degrees of sensitivity and specificity. Since quantification of the intensity of CTS is a crucial step in the electrodiagnostic procedure and is important for defining prognosis and therapeutic measures, such an expert system can be of immense use in those regions where the service of such specialists may not be readily available. It may also prove useful in combination with other systems in providing diagnostic and predictive medical opinions and can add value if introduced into the routine clinical consultations to arrive at the most accurate medical diagnosis in a timely manner. Reeda Kunhimangalam, Sujith Ovallath, and Paul K. Joseph Copyright © 2013 Reeda Kunhimangalam et al. All rights reserved. An Alternative Approach to Protein Folding Mon, 02 Sep 2013 14:02:41 +0000 A diffusion theory-based, all-physical ab initio protein folding simulation is described and applied. The model is based upon the drift and diffusion of protein substructures relative to one another in the multiple energy fields present. Without templates or statistical inputs, the simulations were run at physiologic and ambient temperatures (including pH). Around 100 protein secondary structures were surveyed, and twenty tertiary structures were determined. Greater than 70% of the secondary core structures with over 80% alpha helices were correctly identified on protein ranging from 30 to 200 amino-acid sequence. The drift-diffusion model predicted tertiary structures with RMSD values in the 3–5 Angstroms range for proteins ranging 30 to 150 amino acids. These predictions are among the best for an all ab initio protein simulation. Simulations could be run entirely on a desktop computer in minutes; however, more accurate tertiary structures were obtained using molecular dynamic energy relaxation. The drift-diffusion model generated realistic energy versus time traces. Rapid secondary structures followed by a slow compacting towards lower energy tertiary structures occurred after an initial incubation period in agreement with observations. Yeona Kang and Charles M. Fortmann Copyright © 2013 Yeona Kang and Charles M. Fortmann. All rights reserved. Computational Elucidation of Structural Basis for Ligand Binding with Leishmania donovani Adenosine Kinase Wed, 24 Jul 2013 11:50:22 +0000 Enzyme adenosine kinase is responsible for phosphorylation of adenosine to AMP and is crucial for parasites which are purine auxotrophs. The present study describes development of robust homology model of Leishmania donovani adenosine kinase to forecast interaction phenomenon with inhibitory molecules using structure-based drug designing strategy. Docking calculation using reported organic small molecules and natural products revealed key active site residues such as Arg131 and Asp16 for ligand binding, which is consistent with previous studies. Molecular dynamics simulation of ligand protein complex revealed the importance of hydrogen bonding with active site residues and solvent molecules, which may be crucial for successful development of drug candidates. Precise role of Phe168 residue in the active site was elucidated in this report that provided stability to ligand-protein complex via aromatic-π contacts. Overall, the present study is believed to provide valuable information to design a new compound with improved activity for antileishmanial therapeutics development. Rajiv K. Kar, Md. Yousuf Ansari, Priyanka Suryadevara, Bikash R. Sahoo, Ganesh C. Sahoo, Manas R. Dikhit, and Pradeep Das Copyright © 2013 Rajiv K. Kar et al. All rights reserved. Elucidation of Novel Structural Scaffold in Rohu TLR2 and Its Binding Site Analysis with Peptidoglycan, Lipoteichoic Acid and Zymosan Ligands, and Downstream MyD88 Adaptor Protein Mon, 15 Jul 2013 13:31:20 +0000 Toll-like receptors (TLRs) play key roles in sensing wide array of microbial signatures and induction of innate immunity. TLR2 in fish resembles higher eukaryotes by sensing peptidoglycan (PGN) and lipoteichoic acid (LTA) of bacterial cell wall and zymosan of yeasts. However, in fish TLR2, no study yet describes the ligand binding motifs in the leucine rich repeat regions (LRRs) of the extracellular domain (ECD) and important amino acids in TLR2-TIR (toll/interleukin-1 receptor) domain that could be engaged in transmitting downstream signaling. We predicted these in a commercially important freshwater fish species rohu (Labeo rohita) by constructing 3D models of TLR2-ECD, TLR2-TIR, and MyD88-TIR by comparative modeling followed by 40 ns (nanosecond) molecular dynamics simulation (MDS) for TLR2-ECD and 20 ns MDS for TLR2-TIR and MyD88-TIR. Protein (TLR2-ECD)–ligands (PGN, LTA, and zymosan) docking in rohu by AutoDock4.0, FlexX2.1, and GOLD4.1 anticipated LRR16–19, LRR12–14, and LRR20-CT as the most important ligand binding motifs. Protein (TLR2-TIR)—protein (MyD88-TIR) interaction by HADDOCK and ZDOCK predicted BB loop, αB-helix, αC-helix, and CD loop in TLR2-TIR and BB loop, αB-helix, and CD loop in MyD88-TIR as the critical binding domains. This study provides ligands recognition and downstream signaling. Bikash Ranjan Sahoo, Madhubanti Basu, Banikalyan Swain, Manas Ranjan Dikhit, Pallipuram Jayasankar, and Mrinal Samanta Copyright © 2013 Bikash Ranjan Sahoo et al. All rights reserved. Hyperpolarization-Activated Current, , in Mathematical Models of Rabbit Sinoatrial Node Pacemaker Cells Mon, 08 Jul 2013 15:01:07 +0000 A typical feature of sinoatrial (SA) node pacemaker cells is the presence of an ionic current that activates upon hyperpolarization. The role of this hyperpolarization-activated current, , which is also known as the “funny current” or “pacemaker current,” in the spontaneous pacemaker activity of SA nodal cells remains a matter of intense debate. Whereas some conclude that plays a fundamental role in the generation of pacemaker activity and its rate control, others conclude that the role of is limited to a modest contribution to rate control. The ongoing debate is often accompanied with arguments from computer simulations, either to support one's personal view or to invalidate that of the antagonist. In the present paper, we review the various mathematical descriptions of that have been used in computer simulations and compare their strikingly different characteristics with our experimental data. We identify caveats and propose a novel model for based on our experimental data. Arie O. Verkerk and Ronald Wilders Copyright © 2013 Arie O. Verkerk and Ronald Wilders. All rights reserved. Transmission Model of Hepatitis B Virus with the Migration Effect Mon, 24 Jun 2013 08:48:09 +0000 Hepatitis B is a globally infectious disease. Mathematical modeling of HBV transmission is an interesting research area. In this paper, we present characteristics of HBV virus transmission in the form of a mathematical model. We analyzed the effect of immigrants in the model to study the effect of immigrants for the host population. We added the following flow parameters: “the transmission between migrated and exposed class” and “the transmission between migrated and acute class.” With these new features, we obtained a compartment model of six differential equations. First, we find the basic threshold quantity Ro and then find the local asymptotic stability of disease-free equilibrium and endemic equilibrium. Furthermore, we find the global stability of the disease-free and endemic equilibria. Previous similar publications have not added the kind of information about the numerical results of the model. In our case, from numerical simulation, a detailed discussion of the parameters and their numerical results is presented. We claim that with these assumptions and by adding the migrated class, the model informs policy for governments, to be aware of the immigrants and subject them to tests about the disease status. Immigrants for short visits and students should be subjected to tests to reduce the number of immigrants with disease. Muhammad Altaf Khan, Saeed Islam, Muhammad Arif, and Zahoor ul Haq Copyright © 2013 Muhammad Altaf Khan et al. All rights reserved. Global Stability of Vector-Host Disease with Variable Population Size Sun, 09 Jun 2013 15:25:18 +0000 The paper presents the vector-host disease with a variability in population. We assume, the disease is fatal and for some cases the infected individuals become susceptible. We first show the local and global stability of the disease-free equilibrium, for the case when, the disease free-equilibrium of the model is both locally as well as globally stable. For , the disease persistence occurs. The endemic equilibrium is locally as well as globally asymptotically stable for . Numerical results are presented for the justifications of theoratical results. Muhammad Altaf Khan, Saeed Islam, Sher Afzal Khan, and Gul Zaman Copyright © 2013 Muhammad Altaf Khan et al. All rights reserved.