ArticleBriefings in bioinformatics2023
AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- Identification and Verification of SLC6A15 Involved in Keloid via Bioinformatics Analysis and Machine Learning.Biochemical genetics · 2026Article
- LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings.Frontiers in bioinformatics · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- [A heterogeneous graph method integrating multi-layer semantics and topological information for improving drug-target interaction prediction].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges.Journal of assisted reproduction and genetics · 2025Review
- Screening Results and Performance of the CBCL Using a two-step Procedure for Mental Disorders in Chinese Children and Adolescents: a five-year Survey.The Psychiatric quarterly · 2025Article
- Relational similarity-based graph contrastive learning for DTI prediction.Briefings in bioinformatics · 2025Article
- Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.BMC genomics · 2024Article
- MGACL: Prediction Drug-Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning.Biomolecules · 2024Article
- Attribute-guided prototype network for few-shot molecular property prediction.Briefings in bioinformatics · 2024Article
- A Health Monitoring Model for Circulation Water Pumps in a Nuclear Power Plant Based on Graph Neural Network Observer.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.