Bi-stage hierarchical selection of pathway genes for cancer progression using a swarm based computational approach

Abstract

Background

Understanding of molecular mechanism, lying beneath the carcinogenic expression, is very essential for early and accurate detection of the disease. It predicts various types of irregularities and results in effective drug selection for the treatment. Pathway information plays an important role in mapping of genotype information to phenotype parameters. It helps to find co-regulated gene groups whose collective expression is strongly associated with the cancer development. Method

In this paper, we have proposed a bi-stage hierarchical swarm based gene selection technique which combines two methods, proposed in this paper for the first time. First one is a multi-fitness discrete particle swarm optimization (MFDPSO) based feature selection procedure, having multiple fitness functions. This technique uses multi-filtering based gene selection procedure. On top of it, a new blended Laplacian artificial bee colony algorithm (BLABC) is proposed and it is used for automatic clustering of the selected genes obtained from the first procedure. We have performed 10 times 10-fold cross validation and compared our proposed method with various statistical and swarm based gene selection techniques for different popular cancer datasets. Result

Experimental results show that the proposed method as a whole performs significantly well. The MFDPSO based system in combination with BLABC generates a good subset of pathway markers which provides more effective insight into the gene-disease association with high accuracy and reliability.