MOVE: Integrating Multi-source Information for Predicting DTI via Cross-view Contrastive Learning
Author:
Affiliation:
1. Sichuan University,School of Computer Science,Chengdu,China
2. West China Hospital, Sichuan University,The West China Biomedical Big Data Center,Chengdu,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995438.pdf?arnumber=9995438
Reference22 articles.
1. GraphDTA: predicting drug–target binding affinity with graph neural networks
2. Collaborative matrix factorization with multiple similarities for predicting drug-target interactions
3. DeepDTA: deep drug–target binding affinity prediction
4. WideDTA: Prediction of drug-target binding affinity;Ozturk;CoRR,2019
5. Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
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1. CCL-DTI: contributing the contrastive loss in drug–target interaction prediction;BMC Bioinformatics;2024-01-30
2. Cross-view contrastive representation learning approach to predicting DTIs via integrating multi-source information;Methods;2023-10
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