title: Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme.
abstract: Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.
authors: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors: Weng Howe
authors: WH
authors: Chan
authors: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: saberi@utm.my.
authors: Mohd Saberi
authors: MS
authors: Mohamad
authors: Faculty of Creative Technology & Heritage, Universiti Malaysia Kelantan, Locked Bag 01, Bachok, 16300 Kota Bharu, Kelantan, Malaysia.
authors: Safaai
authors: S
authors: Deris
authors: College of Information Technology, United Arab Emirate University, Al Ain 15551, United Arab Emirates.
authors: Nazar
authors: N
authors: Zaki
authors: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Malaysia.
authors: Shahreen
authors: S
authors: Kasim
authors: Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan.
authors: Sigeru
authors: S
authors: Omatu
authors: Biomedical Research Institute of Salamanca/BISITE Research Group, University of Salamanca, Salamanca, Spain.
authors: Juan Manuel
authors: JM
authors: Corchado
authors: College of Information Technology, United Arab Emirate University, Al Ain 15551, United Arab Emirates.
authors: Hany
authors: H
authors: Al Ashwal
mesh_terms: D000818:Animals; D017209:Apoptosis / Q000235:genetics; D002453:Cell Cycle / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D051379:Mice; D046228:Microarray Analysis; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D060388:Support Vector Machine; D059467:Transcriptome / Q000235:genetics*
keywords: Artificial intelligence; Bioinformatics; Informative genes; Pathway-based microarray analysis; Penalized support vector machine; Penalty function; Weighting scheme