Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920762 | Computers in Biology and Medicine | 2016 | 44 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Weng Howe Chan, Mohd Saberi Mohamad, Safaai Deris, Nazar Zaki, Shahreen Kasim, Sigeru Omatu, Juan Manuel Corchado, Hany Al Ashwal,