Informative gene selection using Adaptive Analytic Hierarchy Process (A2HP)

Gene expression dataset derived from microarray experiments are marked by large number of genes, which contains the gene expression values at different sample conditions/time-points. Selection of informative genes from these large datasets is an issue of major concern for various researchers and biologists. In this study, we propose a gene selection and dimensionality reduction method called Adaptive Analytic Hierarchy Process (A2HP). Traditional analytic hierarchy process is a multiple-criteria based decision analysis method whose result depends upon the expert knowledge or decision makers. It is mainly used to solve the decision problems in different fields. On the other hand, A2HP is a fused method that combines the outcomes of five individual gene selection ranking methods t-test, chi-square variance test, z-test, wilcoxon test and signal-to-noise ratio (SNR). At first, the preprocessing of gene expression dataset is done and then the reduced number of genes obtained, will be fed as input for A2HP. A2HP utilizes both quantitative and qualitative factors to select the informative genes. Results demonstrate that A2HP selects efficient number of genes as compared to the individual gene selection methods. The percentage of deduction in number of genes and time complexity are taken as the performance measure for the proposed method. And it is shown that A2HP outperforms individual gene selection methods.