Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.

ABSTRACT: Determining the target genes that interact with drugs-drug-target interactions-plays an important role in drug discovery. Identification of drug-target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug-target interactions. We cons

SUBMITTER: Xuan P 

PROVIDER: S-EPMC6555260 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature