Neuro-fuzzy methodology for selecting genes mediating lung cancer

Abstract

In this article, we describe neuro-fuzzy models under supervised and unsupervised learning for selecting a few possible genes mediating a disease. The methodology involves grouping of genes based on correlation coefficient using microarray gene expression patterns. The most important group is selected using existing neuro-fuzzy systems [1,2,3,4,5]. Finally, a few possible genes are selected from the most important group using the aforesaid neuro-fuzzy systems. The effectiveness of the methodology has been demonstrated on lung cancer gene expression data sets. The superiority of the methodology has been established with four existing gene selection methods like SAM, SNR, NA and BR. The enrichment of each gene ontology category of the resulting genes was calculated by its P-value. The genes output the low P-value, and indicate that they are biologically significant. According to the methodology, we have found more true positive genes than the other existing algorithms. © 2011 Springer-Verlag Berlin Heidelberg.