Rough hypercuboid and modified kulczynski coefficient for disease gene identification

Abstract

The most important objective of human genetics research is the discovery of genes associated to a disease. In this respect, a new algorithm for gene selection is presented, which integrates wisely the information from expression profiles of genes and protein-protein interaction networks. The rough hypercuboid approach is used for identifying differentially expressed genes from the microarray, while a new measure of similarity is proposed to exploit the interaction network of proteins and therefore, determine the pairwise functional similarity of proteins. The proposed algorithm aims to maximize the relevance and functional similarity, and utilizes it as an objective function for the identification of a subset of genes that it predicts as disease genes. The performance of the proposed algorithm is compared with other related methods using some cancer associated data sets.