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Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

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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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  • Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews written by leaders in the field covering a range of the current topics in medicinal chemistry. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.

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