Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions.

ABSTRACT: The continuous emergence of drug-target interaction data provides an opportunity to construct a biological network for systematically discovering unknown interactions. However, this is challenging due to complex and heterogeneous correlations between drug and target. Here, we describe a heterogeneous hypergraph-based framework for drug-target interaction (HHDTI) predictions by modeling biological networks through a hypergraph, where each vertex represents a drug or a target and a hyperedge indicates existing similar interactions or associations between the connected vertices. The hypergraph is then trained to generate suitably structured embeddings for discovering unknown interactions. Comprehensive experiments performed on four public datasets demonstrate that HHDTI achieves significant a

SUBMITTER: Ruan D 

PROVIDER: S-EPMC8672193 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature