Pathway-BasedFeature Selection Algorithm for Cancer Microarray Data. (11th January 2010)

• Record Type: Journal Article
• Authors: Bandyopadhyay, Nirmalya; Kahveci, Tamer; Goodison, Steve; Sun, Y.; Ranka, Sanjay
• Other Names: Can Tolga Academic Editor.
• Is Part Of: Advances in bioinformatics. Volume 2009(2009)
• Publication Date: 2010-01-11
• Subjects: Bioinformatics -- Periodicals; Bioinformatics; Computational Biology -- Periodicals; Periodicals; 570.285
• DOI: 10.1155/2009/532989
• Languages: English
• ISSNs: 1687-8027

Abstract

Abstract : Classification of cancers based on gene expressions produces better accuracy when compared to that of the clinical markers. Feature selection improves the accuracy of these classification algorithms by reducing the chance of overfitting that happens due to large number of features. We develop a new feature selection method called Biological Pathway-based Feature Selection (BPFS) for microarray data. Unlike most of the existing methods, our method integrates signaling and gene regulatory pathways with gene expression data to minimize the chance of overfitting of the method and to improve the test accuracy. Thus, BPFS selects a biologically meaningful feature set that is minimally redundant. Our experiments on published breast cancer datasets demonstrate that all of the top 20 genes found by our method are associated with cancer. Furthermore, the classification accuracy of our signature is up to 18% better than that of vant Veers 70 gene signature, and it is up to 8% better accuracy than the best published feature selection method, I-RELIEF.