An eigenvalue transformation technique for predicting drug-target interaction.

ABSTRACT: The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better perfor

SUBMITTER: Kuang Q 

PROVIDER: S-EPMC4563363 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature