DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

ABSTRACT:

Motivation

Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate.

Results

We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model u

SUBMITTER: Olayan RS 

PROVIDER: S-EPMC5998943 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature