Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme. (1st October 2016)

• Record Type: Journal Article
• Authors: Chan, Weng Howe; Mohamad, Mohd Saberi; Deris, Safaai; Zaki, Nazar; Kasim, Shahreen; Omatu, Sigeru; Corchado, Juan Manuel; Al Ashwal, Hany
• Is Part Of: Computers in biology and medicine. Volume 77(2016)
• Page Start: 102
• Page End: 115
• Publication Date: 2016-10-01
• Subjects: Artificial intelligence -- Bioinformatics -- Informative genes -- Pathway-based microarray analysis -- Penalized support vector machine -- Weighting scheme -- Penalty function; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.08.004
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: An improved penalized support vector machine with weighting scheme for pathway analysis. The weighting scheme based on two-sample t -test for highlighting difference between two conditions. Identification of informative pathways and genes within the pathways that are related to the phenotype under study.