Predicting drug target interactions using meta-path-based semantic network analysis.

ABSTRACT:

Background

In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.

Results

Supervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of t

SUBMITTER: Fu G 

PROVIDER: S-EPMC4830032 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature