title: Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.
abstract: Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.
authors.affiliation: School of Computing, Clemson University, Clemson, SC, USA.
authors.forename: Xumeng
authors.initials: X
authors.lastname: Li
authors.forename: Frank A
authors.initials: FA
authors.lastname: Feltus
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Sun
authors.forename: James Z
authors.initials: JZ
authors.lastname: Wang
authors.forename: Feng
authors.initials: F
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D003198:Computer Simulation; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D060066:Protein Interaction Maps; D012680:Sensitivity and Specificity