title: Predicting disease phenotypes based on the molecular networks with condition-responsive correlation.
abstract: Network-based methods using molecular interaction networks integrated with gene expression profiles have been proposed to solve problems, which arose from smaller number of samples compared with the large number of predictors. However, previous network-based methods, which have focused only on expression levels of proteins, nodes in the network through the identification of condition-responsive interactions. We propose a novel network-based classification, which focuses on both nodes with discriminative expression levels and edges with Condition-Responsive Correlations (CRCs) across two phenotypes. We found that modules with condition-responsive interactions provide candidate molecular models for diseases and show improved performances compared conventional gene-centric classification methods.
authors.affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. sejoon@biosoft.kaist.ac.kr
authors.forename: Sejoon
authors.initials: S
authors.lastname: Lee
authors.forename: Eunjung
authors.initials: E
authors.lastname: Lee
authors.forename: Kwang H
authors.initials: KH
authors.lastname: Lee
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism / Q000556:secondary; D019295:Computational Biology; D003198:Computer Simulation; D057225:Data Mining; D016208:Databases, Factual; D004194:Disease / Q000145:classification* / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008297:Male; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D010641:Phenotype; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism