MRLF-DDI: A Multi-view Representation Learning Framework for Drug-Drug Interaction Event Prediction.

IF 6.8 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
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.

MRLF-DDI:药物-药物相互作用事件预测的多视图表示学习框架。
准确预测药物相互作用事件对提高用药安全性和指导临床决策至关重要。然而,现有的基于图神经网络(GNN)的方法往往难以有效地整合多视图特征并推广到新型或未充分研究的药物。为了解决这些限制,我们提出了MRLF-DDI,这是一个多视图表示学习框架,可以联合建模来自单个药物特征、局部相互作用背景和全局相互作用模式的信息。MRLF-DDI引入了富含键角信息的原子级结构特征的使用-标志着这种几何感知特征首次纳入DDIE预测。进一步采用多粒度GNN和门控知识转移策略来增强特征学习和冷启动泛化。在基准数据集上的大量实验表明,MRLF-DDI在热启动和冷启动场景下都具有优异的性能。案例研究和可视化分析进一步强调了其在确定临床相关ddie方面的实用价值。MRLFDDI的代码可从https://github.com/jianzhong123/MRLFDDI获得。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
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.
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