DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing.

ABSTRACT:

Background

Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.

Results

This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a

SUBMITTER: Ghorbanali Z 

PROVIDER: S-EPMC10548718 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature