title: Discovering Pair-wise Synergies in Microarray Data.
abstract: Informative gene selection can have important implications for the improvement of cancer diagnosis and the identification of new drug targets. Individual-gene-ranking methods ignore interactions between genes. Furthermore, popular pair-wise gene evaluation methods, e.g. TSP and TSG, are helpless for discovering pair-wise interactions. Several efforts to discover pair-wise synergy have been made based on the information approach, such as EMBP and FeatKNN. However, the methods which are employed to estimate mutual information, e.g. binarization, histogram-based and KNN estimators, depend on known data or domain characteristics. Recently, Reshef et al. proposed a novel maximal information coefficient (MIC) measure to capture a wide range of associations between two variables that has the property of generality. An extension from MIC(X; Y) to MIC(X1; X2; Y) is therefore desired. We developed an approximation algorithm for estimating MIC(X1; X2; Y) where Y is a discrete variable. MIC(X1; X2; Y) is employed to detect pair-wise synergy in simulation and cancer microarray data. The results indicate that MIC(X1; X2; Y) also has the property of generality. It can discover synergic genes that are undetectable by reference feature selection methods such as MIC(X; Y) and TSG. Synergic genes can distinguish different phenotypes. Finally, the biological relevance of these synergic genes is validated with GO annotation and OUgene database.
authors.affiliation: Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha, Hunan, 410128, China.|Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, Hunan, 410128, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Orient Science &Technology College of Hunan Agricultural University, Changsha, Hunan, 410128, China.
authors.forename: Dan
authors.initials: D
authors.lastname: Cao
authors.affiliation: College of Resources &Environment, Hunan Agricultural University, Changsha, Hunan, 410128, China.|Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Gao
authors.affiliation: Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha, Hunan, 410128, China.|Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, Hunan, 410128, China.
authors.forename: Zheming
authors.initials: Z
authors.lastname: Yuan
mesh_terms: D056808:Biostatistics / Q000379:methods*; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D046228:Microarray Analysis / Q000379:methods*; D009369:Neoplasms / Q000473:pathology*