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

Jongsoo Keum, Hojung Nam, Alexey Porollo
2017 PLoS ONE  
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
more » ... nteractions. 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 unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are selftrained and final local classification models are constructed.
doi:10.1371/journal.pone.0171839 pmid:28192537 pmcid:PMC5305209 fatcat:irfzswf3u5cjrbenv2moptqi6y