Identification of drug-target interaction by a random walk with restart method on an interactome network.

ABSTRACT:

Background

Identification of drug-target interactions acts as a key role in drug discovery. However, identifying drug-target interactions via in-vitro, in-vivo experiments are very laborious, time-consuming. Thus, predicting drug-target interactions by using computational approaches is a good alternative. In recent studies, many feature-based and similarity-based machine learning approaches have shown promising results in drug-target interaction predictions. A previous study showed that accounting connectivity information of drug-drug and protein-protein interactions increase performances of prediction by the concept of 'guilt-by-association'. However, the approach that only considers directly connected nodes often misses the information that could be derived from distance nodes. T

SUBMITTER: Lee I 

PROVIDER: S-EPMC5998759 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature