Towards interpretable drug interaction prediction via dual-stage attention and Bayesian calibration with active learning

Towards interpretable drug interaction prediction via dual-stage attention and Bayesian calibration with active learning

Background Drug-drug interactions (DDIs) account for 17–23% of adverse drug reactions leading to hospitalization, with over 74,000 DDI-related events reported in the FDA Adverse Event Reporting System (FAERS) during 2023. While recent computational methods focus on improving prediction accuracy, the...

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Bibliographic Details
Main Authors: Rongpei Li, Yufang Zhang, Heqi Sun, Shenggeng Lin, Guihua Jia, Yitian Fang, Chen Zhang, Xiaotong Song, Jianwei Zhao, Lyubin Hu, Yajing Yuan, Xueying Mao, Jiayi Li, Aman Kaushik, Dandan An, Dongqing Wei
Format: Article
Language:English
Published: PeerJ Inc. 2025-04-01
Series:PeerJ Computer Science
Subjects:
Drug-drug interactions
Dual-stage attention networks
Bayesian calibration
Active learning
LSTM networks
Network pharmacology
Online Access:https://peerj.com/articles/cs-2847.pdf
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