An improved gSVM-SCADL2 with firefly algorithm for identification of informative genes and pathways. (2016)

• Record Type: Journal Article
• Authors: Chan, Weng Howe; Mohamad, Mohd Saberi; Deris, Safaai; Corchado, Juan Manuel; Omatu, Sigeru; Ibrahim, Zuwairie; Kasim, Shahreen
• Is Part Of: International journal of bioinformatics research and applications. Volume 12:Number 1(2016)
• Page Start: 72
• Page End: 93
• Publication Date: 2016
• Subjects: pathway-based microarray analysis -- gene selection -- penalised SVM -- support vector machines -- bioinformatics -- artificial intelligence -- firefly algorithm -- genes -- pathways -- lung cancer -- gender; Bioinformatics -- Periodicals; 570.285
• Languages: English
• ISSNs: 1744-5485

Abstract

Incorporation of pathway knowledge into microarray analysis has been favoured by researchers owing to the improved biological interpretation of the analysis outcome. However, most of the pathway data are manually curated without specific biological context. Inclusion of non-informative genes in the analysis of context specific microarray data could lead to classifier with poor discriminative power. Thus, one of the main challenges is how to effectively identify informative genes from the pathway data. This paper proposes a firefly optimised penalised support vector machine with SCADL2 penalty function (SVM-SCADL2-FFA) in optimising tuning parameters for each pathway for efficient identification of informative genes and pathways. Experiments are done on lung cancer and gender data sets. Tenfold CV is used to evaluate the performance in terms of accuracy, specificity, sensitivity and F-score. The identified informative genes are validated through online databases. Our proposed method shows consistent improvements compared to previous works.