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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.
Chief Editor, Professor Tramontana, is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.
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A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification
The identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. However, the traditional high-throughput techniques based on clinical trials are costly, cumbersome, and time-consuming for identifying DTIs. Hence, new intelligent computational methods are urgently needed to surmount these defects in predicting DTIs. In this paper, we propose a novel computational method that combines position-specific scoring matrix (PSSM), elastic net based sparse features extraction, and rotation forest (RF) classifier. Specifically, we converted each protein primary sequence into PSSM, which contains biological evolutionary information. Then we extract the hidden sparse feature descriptors in PSSM by elastic net based sparse feature extraction method (ESFE). After that, we fuse them with the features of drug, which are represented by molecular fingerprints. Finally, rotation forest classifier works on detecting the potential drug-target interactions. When performing the proposed method by the experiments of fivefold cross validation (CV) on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor datasets, this method achieves average accuracies of 90.32%, 88.91%, 80.65%, and 79.73%, respectively. We also compared the proposed model with the state-of-the-art support vector machine (SVM) classifier and other effective methods on the same datasets. The comparison results distinctly indicate that the proposed model possesses the efficient and robust ability to predict DTIs. We expect that the new model will be able to take effects on predicting massive DTIs.
The Value and Clinical Significance of Tumor Marker Detection in Cervical Cancer
When it comes to cervical cancer, it is the most common malignancy in gynecology. This study aimed to investigate the concomitant status of miRNA-9-5p in cervical cancer and explore its potential mechanism for treating cervical cancer. The levels of miRNA-9-5p, CA125, CA199, and CEA expression were detected by RT-PCR, and the downstream target genes regulated by miRNA-9-5p were screened by the Venn map. Cytoscape was utilized to find the binding sites of the two genes, and luciferase reporter assay verified the direct regulation of miRNA-9-5p and CXCR4; the CCK-8 assay detected its regulation on cell proliferation, and the expression of miRNA-9-5p, CXCR4, PCNA, Ki67 mRNA, and proteins was detected by RT-PCR and western blot. The expression of miRNA-9-5p was decreased, while the levels of CA125, CA199, and CEA were increased in the model group. The database predicts that CXCR4 is a gene regulated by miRNA-9-5p. The luciferase reporter gene results indicated that miRNA-9-5p could directly regulate the expression of CXCR4 and miRNAs are detected by intracellular transfer inhibitors. In total, MiRNA-9-5p can be utilized as a biological marker for cervical cancer that may inhibit cancer cells’ proliferation by inhibiting the expression of the CXCR4 gene and protein.
Analyzing the Classification Techniques for Bulk of Cursive Languages Data: An Overview
The remarkable growth of texts both in online and offline is becoming a challenging issue which need exploration for further research. Diversities of regional and cultural changes have produced diverse languages as a source of communication. Variations of styles are existing for handwritten texts which is due to varying writing styles. The research area of text recognition is matured which has increased a number of directions in the area of research. A detail report of the existing literature is needed which can help practitioners and researchers to use the existing evidence and provide new solutions for identification of cursive languages and to optimize the ability of recognition for cursive text. For facilitating the researchers and practitioners by providing in-depth analysis of the existing literature, the proposed study provide a detail report through which researchers can get benefit of the literature and devise new solutions. This study is based on searching various popular libraries for identifying relevant materials associated with the proposed study.
A Clustering Algorithm via Density Perception and Hierarchical Aggregation Based on Urban Multimodal Big Data for Identifying and Analyzing Categories of Poverty-Stricken Households in China
Nowadays, urban multimodal big data are freely available to the public due to the growing number of cities, which plays a critical role in many fields such as transportation, education, medical treatment, and land resource management. The successful completion of poverty-relief work can greatly improve the quality of people’s life and ensure the sustainable development of the society. Poverty is a severe challenge for human society. It is of great significance to apply machine learning to mine different categories of poverty-stricken households and further provide decision support for poverty alleviation. Traditional poverty alleviation methods need to consume a lot of manpower, material resources, and financial resources. Based on the density-based spatial clustering of applications with noise (DBSCAN), this paper designs the hierarchical DBSCAN clustering algorithm to identify and analyze the categories of poverty-stricken households in China. First, the proposed method adjusts the neighborhood radius dynamically for dividing the data space into several initial clusters with different densities. Then, neighbor clusters are identified by the border and inner distances constantly and aggregated recursively to form new clusters. Based on the idea of division and aggregation, the proposed method can recognize clusters of different forms and deal with noises effectively in the data space with imbalanced density distribution. The experiments indicate that the method has the ideal performance of clustering, which identifies the commonness and difference in characteristics of poverty-stricken households reasonably. In terms of the specific indicator “Accuracy,” the accuracy increases by 2.3% compared with other methods.
The Research on Huanglian Jiedu Decoction against Atopic Dermatitis
Objective. Study on the pharmacodynamic basis and mechanism of Huanglian Jiedu Decoction against atopic dermatitis (AD). Methods. Based on network pharmacology, the targets of Huanglian Jiedu Decoction and AD were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), SwissTargetPrediction databases, and the database of Online Mendelian Inheritance in Man (OMIM), Therapeutic Targets Database (TTD) and the Comparative Toxicogenomics Database (CTD); then, “chemical composition-target-related pathway-disease target” network graph of Huanglian Jiedu Decoction against AD was constructed by using STRING and Cytoscape software. In combination with in vitro experiments, the levels of IL-4, IL-6, and IL-10 in T cells were determined by ELISA; the pharmacodynamic basis and mechanism of Huanglian Jiedu Decoction against AD were preliminarily explored. Results. 81 active ingredients in Huanglian Jiedu Decoction were screened by network pharmacology, 31 of which were related to atopic dermatitis, corresponding to 12 target proteins. A total of 14 pathways were obtained by KEGG pathway analysis, and 8 were associated with atopic dermatitis. Compared with the control group, 20 and 40 µg/ml of Huanglian Jiedu Decoction could significantly reduce the contents of IL-4, IL-6, and IL-10 in T lymphocytes of mice with atopic dermatitis (). Conclusion. Huanglian Jiedu Decoction can act against AD by multicomponent, multitarget, and multichannel mode of action.
An Algorithm of Occlusion Detection for the Surveillance Camera
As more and more surveillance cameras are deployed in the Internet of Things, it takes more and more work to ensure the cameras are not occluded. An algorithm of detecting whether the surveillance camera is occluded is proposed by comparing the similarity of the images in this paper. Firstly, the background modeling method based on frame difference is improved. The combination method of the background difference and frame difference is proposed, and the experimental results showed that the combination algorithm can extract the background image of the video more quickly and accurately. Secondly, the LBP (Local Binary Patterns) algorithm is used to compare the similarity between the background image and the reference image. By changing the window size of the LBP algorithm and setting an appropriate threshold, the actual demands can be satisfied. So, the algorithms proposed in this paper have high application value and practical significance.