title: CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.
abstract: BACKGROUND
  Predication 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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  For 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/ .
authors.affiliation: Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. zeeshan_gillani100@hotmail.com.
authors.forename: Zeeshan
authors.initials: Z
authors.lastname: Gillani
authors.affiliation: Institute of Pharmacology, Toxicology and Biochemical Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. sajidakash@gcuf.edu.pk.|Faculty of Pharmaceutical Sciences, Government College University Faisalabad, Faisalabad, Pakistan. sajidakash@gcuf.edu.pk.
authors.forename: Muhammad Sajid Hamid
authors.initials: MS
authors.lastname: Akash
authors.affiliation: Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. matiur.stat@gmail.com.
authors.forename: M D Matiur
authors.initials: MD
authors.lastname: Rahaman
authors.affiliation: Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. mchen@zju.edu.cn.
authors.forename: Ming
authors.initials: M
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D029968:Escherichia coli Proteins / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D015398:Signal Transduction; D012984:Software; D060388:Support Vector Machine; D049490:Systems Biology
chemical_list: D029968:Escherichia coli Proteins; D029701:Saccharomyces cerevisiae Proteins