Drug repositioning based on residual attention network and free multiscale adversarial training

• Published in: BMC bioinformatics Vol. 25; no. 1; pp. 261 - 21
• Main Authors: Li, Guanghui, Li, Shuwen, Liang, Cheng, Xiao, Qiu, Luo, Jiawei
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.08.2024; BioMed Central; BMC
• Subjects: Adversarial training; Algorithms; Collaboration; Computational Biology - methods; Cost analysis; Deep learning; Disease; Drug development; Drug discovery; Drug Repositioning - methods; Drug-disease association; Drugs; Graph attention network; Graph autoencoder; Humans; Information processing; Machine learning; Methods; MicroRNAs; Multilayers; Neighborhoods; Networks; Neural networks; Neural Networks, Computer; Nodes; Pathogenesis; Pharmaceutical research; Predictions; Proteins; Residual network; Semantics; Technology application; Training; China; Graph autoencoder; Adversarial training; Graph attention network; Residual network; Drug-disease association

Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exp...