药物重新定位的自动协作学习

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yi Wang , Yajie Meng , Chang Zhou , Xianfang Tang , Pan Zeng , Chu Pan , Qiang Zhu , Bengong Zhang , Junlin Xu
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引用次数: 0

摘要

药物重新定位旨在为现有药物确定新的治疗用途,从而加快研发速度并降低成本。传统的湿实验室实验成本高昂,而计算方法则提供了一种低成本、高效率的替代方法。尽管这些方法潜力巨大,但该领域的大多数研究都不加批判地采用了图神经网络(GNN)的标准信息传递机制,从而限制了对预测准确性的协同效应评估。在本文中,我们引入了一个新模型,即药物重新定位的自动协作学习框架。首先,我们提出了一种衡量邻居之间交互水平的指标,并将其与 GNN 固有的消息传递机制相结合,从而增强了各种协作效应对预测准确性的影响。此外,我们还引入了一种先进的对比学习技术,以调整疾病-药物关联空间和自定义邻居空间之间的特征一致性。这种方法利用了不同特征维度的固有规律性,最大限度地减少了特征冗余。在三个基准数据集上进行的广泛实验表明,与各种最先进的方法相比,这种新型模型有了实质性的改进。案例研究进一步凸显了这一模型的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic collaborative learning for drug repositioning
Drug repositioning seeks to identify new therapeutic uses for existing drugs, accelerating development and reducing costs. While traditional wet lab experiments are costly, computational methods offer a low-cost, efficient alternative. Despite their potential, most research in this field has uncritically employed the standard message-passing mechanism of Graph Neural Network (GNN), limiting the assessment of collaborative effects on prediction accuracy. In this paper, we introduce a novel model, an automatic collaborative learning framework for drug repositioning. Initially, we propose a metric to measure the interaction levels among neighbors and integrate it with the intrinsic message-passing mechanism of GNN, thereby enhancing the impact of various collaborative effects on prediction accuracy. Furthermore, we introduce an advanced contrastive learning technique to align feature consistency between the disease–drug association space and the customized neighbor space. This approach leverages the inherent regularities across different feature dimensions to minimize feature redundancy. Extensive experiments conducted on three benchmark datasets demonstrate substantial improvements of this novel model over various state-of-the-art methods. Case studies further highlight the practical utility of this model.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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