A non-parameter Ising model for network-based identification of differentially expressed genes in recurrent breast cancer patients

Abstract

Identification of genes and pathways involving in diseases and physiological conditions is a major task in systems biology. In this study, we develop a new non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also propose a simulated annealing algorithm to find the optimal configuration of the Ising model. We test the Ising model to two breast cancer microarray data sets. The results show that more cancer related differentially expressed subnetworks and genes are identified by the Ising model than by the Markov random filed (MRF) model.