Predicting Drug-Target Interactions via Within-Score and Between-Score.

ABSTRACT: Network inference and local classification models have been shown to be useful in predicting newly potential drug-target interactions (DTIs) for assisting in drug discovery or drug repositioning. The idea is to represent drugs, targets, and their interactions as a bipartite network or an adjacent matrix. However, existing methods have not yet addressed appropriately several issues, such as the powerless inference in the case of isolated subnetworks, the biased classifiers derived from insufficient positive samples, the need of training a number of local classifiers, and the unavailable relationship between known DTIs and unapproved drug-target pairs (DTPs). Designing more effective approaches to address those issues is always desirable. In this paper, after presenting better drug similarit

SUBMITTER: Shi JY 

PROVIDER: S-EPMC4620248 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature