title: Using feature selection technique for drug-target interaction networks prediction.
abstract: Elucidating the interaction relationship between target proteins and all drugs is critical for the discovery of new drug targets. However, it is a big challenge to integrate and optimize different feature information into one single "knowledge view" for drug-target interaction prediction. In this article, a feature selection method was proposed to rank the original feature sets. Then, an improved bipartite learning graph method was used to predict four types of drug-target datasets based on the optimized feature subsets. The cross-validation results demonstrate that the proposed method can provide superior performance than previous method on four classes of drug target families.
authors.affiliation: Department of Computer Science & Technology, East China Normal University, Shanghai, 200241, PR China.
authors.forename: W
authors.initials: W
authors.lastname: Yu
authors.forename: Z
authors.initials: Z
authors.lastname: Jiang
authors.forename: J
authors.initials: J
authors.lastname: Wang
authors.forename: R
authors.initials: R
authors.lastname: Tao
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016503:Drug Delivery Systems / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000008:administration & dosage* / Q000737:chemistry
chemical_list: D004364:Pharmaceutical Preparations