SELF-BLM: Prediction of drug-target interactions via self-training SVM.

ABSTRACT: Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes

SUBMITTER: Keum J 

PROVIDER: S-EPMC5305209 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature