Jian Zhong, Haochen Zhao, Xiao Liang, Qichang Zhao, Jianxin Wang
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引用次数: 0
Abstract
Accurately predicting drug-drug interaction events (DDIEs) is critical for improving medication safety and guiding clinical decision-making. However, existing graph neural network (GNN)-based methods often struggle to effectively integrate multi-view features and generalize to novel or understudied drugs. To address these limitations, we propose MRLF-DDI, a multi-view representation learning framework that jointly models information from individual drug features, local interaction contexts, and global interaction patterns. MRLF-DDI introduces the use of atomlevel structural features enriched with bond angle information-marking the first incorporation of this geometryaware feature in DDIE prediction. It further employs a multigranularity GNN and a gated knowledge transfer strategy to enhance feature learning and cold-start generalization. Extensive experiments on benchmark datasets demonstrate that MRLF-DDI achieves superior performance in both warm-start and cold-start scenarios. Case studies and visualization analyses further highlight its practical utility in identifying clinically relevant DDIEs. The code for MRLFDDI is available at https://github.com/jianzhong123/MRLFDDI.
期刊介绍:
IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.