IDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting.

ABSTRACT: Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under

SUBMITTER: Rayhan F 

PROVIDER: S-EPMC5735173 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature