Back to Search
Start Over
Metapath-aggregated heterogeneous graph neural network for drug–target interaction prediction.
- Source :
-
Briefings in Bioinformatics . Jan2023, Vol. 24 Issue 1, p1-17. 17p. - Publication Year :
- 2023
-
Abstract
- Drug–target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data. However, existing GNN-based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network and are incapable of capturing high-order dependencies in the biological heterogeneous graph. In this paper, we propose a metapath-aggregated heterogeneous graph neural network (MHGNN) to capture complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. Specifically, MHGNN enhances heterogeneous graph structure learning and high-order semantics learning by modeling high-order relations via metapaths. Additionally, MHGNN enriches high-order correlations between drug-target pairs (DTPs) by constructing a DTP correlation graph with DTPs as nodes. We conduct extensive experiments on three biological heterogeneous datasets. MHGNN favorably surpasses 17 state-of-the-art methods over 6 evaluation metrics, which verifies its efficacy for DTI prediction. The code is available at https://github.com/Zora-LM/MHGNN-DTI. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DRUG repositioning
*FORECASTING
*ESSENTIAL drugs
Subjects
Details
- Language :
- English
- ISSN :
- 14675463
- Volume :
- 24
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Briefings in Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 161419838
- Full Text :
- https://doi.org/10.1093/bib/bbac578