PPAEDTI: Personalized Propagation Auto-Encoder Model for Predicting Drug-Target Interactions

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 1; pp. 573 - 582
• Main Authors: Li, Yue-Chao, You, Zhu-Hong, Yu, Chang-Qing, Wang, Lei, Wong, Leon, Hu, Lun, Hu, Peng-Wei, Huang, Yu-An
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Area Under Curve; Bioinformatics; Coders; Computational modeling; Computer applications; Customization; Datasets; Diffusion tensor imaging; Drug; Drug development; Drug Interactions; Drug Repositioning; drug-target interaction prediction; Drugs; Humans; Performance evaluation; Performance prediction; personalized propagation auto-encoder; Pharmacology; Predictions; Predictive models; Propagation; protein target; Proteins; Proteins - metabolism; Therapeutic targets

Identifying protein targets for drugs establishes an indispensable knowledge foundation for drug repurposing and drug development. Though expensive and time-consuming, vitro trials are widely employed to discover drug targets, and the existing relevant computational algorithms still cannot satisfy t...