Drug-Target Interaction Prediction Based on Multi-path Graph Convolution and Graph-Level Attention Mechanism
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5692-6_13
Reference27 articles.
1. Ye, Q., et al.: Drug-target interaction prediction via graph auto-encoder and multi-subspace deep neural networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 20, 2647–2658 (2022)
2. Li, A., Lin, X., Yu, H.: Inferring DTIs based on similarity clustering and CaGCN-DTI model from heterogeneous network. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2399–2406. IEEE, Location (2021)
3. Dominguez, C., et al.: HADDOCK: a protein-protein docking approach based on bio-chemical or biophysical information. J. Am. Chem. Soc. 125(7), 1731–1737 (2003)
4. Bagherian, M., et al.: Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Briefings Bioinform. 22(3), 247–269 (2020)
5. Rayhan, F., Ahmed, S., Mousavian, Z., et al.: FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction. Heliyon 6(3) (2020)
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