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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.

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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: 2011-12-01

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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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