CompareSVM: supervised,Support Vector Machine (SVM) inference of gene regularity networks |
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Authors: | Zeeshan Gillani Muhammad Sajid Hamid Akash MD Matiur Rahaman Ming Chen |
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Institution: | .Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058 China ;.Institute of Pharmacology, Toxicology and Biochemical Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China ;.Faculty of Pharmaceutical Sciences, Government College University Faisalabad, Faisalabad, Pakistan |
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Abstract: | BackgroundPredication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.ResultsWe developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.ConclusionsFor network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0395-x) contains supplementary material, which is available to authorized users. |
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Keywords: | Support vector machine Machine running Gene regulatory networks CompareSVM Supervised learning Unsupervised learning CLR (context likelihood to relatedness) |
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