A comprehensive graph neural network method for predicting triplet motifs in disease-drug-gene interactions.

ABSTRACT:

Motivation

The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships. However, existing methods only focus on the triangle representation learning for classification, and fail to further discriminate various motifs of triplets. A comprehensive method is needed to predict the various motifs within triplets, which will uncover new pharmacological mechanisms and improve our understanding of disea

SUBMITTER: Kang C 

PROVIDER: S-EPMC11796092 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature