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 crossvalidation results demonstrate that the proposed method can provide superior performance than previous method on four classes of drug target families.
Keywords: Drug-target interaction; Elucidating; algorithm; biological macromolecules; drug target families; drug targets undetectably; feature selection method; improved bipartite learning graph method; pathological states; target proteins
Document Type: Research Article
Publication date: December 1, 2011
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