Extraction of informative genes from microarray data

Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples compared to huge number of genes, and the noisy nature of microarray data. After selection of some good genes based on signal-to-noise ratio, unsupervised learning like clustering and supervised learning like k-nearest neighbor (kNN) classifier are widely used in cancer researches to correlate the pathological behavior of cancers with the gene expression levels’ differences in cancerous and normal tissues. By applying adaptive searches like Probabilistic Model Building Genetic Algorithm (PMBGA), it may be possible to get a smaller size gene subset that would classify patient samples more accurately than the above methods. In this paper, we propose a new PMBGA based method to extract informative genes from microarray data using Support Vector Machine (SVM) as a classifier. We apply our method to three mi...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Topon Kumar Paul, Hitoshi Iba
• Keywords: Building Genetic Algorithm, GECCO 2005, Microarray Data, Probabilistic Model Building