Journal
CURRENT MEDICINAL CHEMISTRY
Volume 18, Issue 36, Pages 5687-5693Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/092986711798347270
Keywords
Drug-target interaction; feature selection method; improved bipartite learning graph method
Funding
- National Natural Science Foundation of China [30900840]
- Fundamental Research Funds for the Central Universities
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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 cross-validation results demonstrate that the proposed method can provide superior performance than previous method on four classes of drug target families.
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