Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Title: Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Volume: 18 Issue: 36

Author(s): W. Yu, Z. Jiang, J. Wang and R. Tao

Affiliation:

Keywords: Drug-target interaction, feature selection method, improved bipartite learning graph method, Elucidating, target proteins, drug target families, biological macromolecules, pathological states, drug targets undetectably, algorithm

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.

Cite this article as:

Yu W., Jiang Z., Wang J. and Tao R., Using Feature Selection Technique for Drug-Target Interaction Networks Prediction, Current Medicinal Chemistry 2011; 18 (36) . https://dx.doi.org/10.2174/092986711798347270