HeteroKGRep:基于异构知识图谱的药物重新定位

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2024-10-19 DOI:10.1016/j.knosys.2024.112638
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引用次数: 0

摘要

开发新药的过程既耗时又耗资,通常需要十多年的时间和数十亿美元才能获得监管部门的批准。此外,新型化合物专利保护的复杂性也给制药创新带来了挑战。药物重新定位为现有药物发掘新的治疗用途提供了另一种策略。以往的重新定位模型由于依赖同质数据源而受到限制,无法充分利用异质生物医学知识图谱中的丰富信息。我们提出的 HeteroKGRep 是一种新型药物重新定位模型,它利用异构图来解决这些局限性。HeteroKGRep 是一个多步骤框架,首先从分层概念关系中生成一个相似性图。然后,在使用异构图神经网络生成节点序列之前,它应用 SMOTE 过度采样来解决类不平衡问题。从网络中提取药物和疾病嵌入,并用于预测。我们对 HeteroKGRep 进行了评估,该图包含本体、路径和文献中的生物医学概念和关系。它在预测再利用机会方面达到了最先进的性能,准确率为 99%,AUC ROC 为 95%,平均精度为 94%。与现有的同质方法相比,HeteroKGRep 利用不同的知识源来丰富表征学习。基于异构图谱,HeteroKGRep 可以发现新的药物-疾病关联,充分利用新药开发。这项工作为利用多模态生物医学数据进行知识引导的药物重新定位建立了一个前景广阔的新范例。
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HeteroKGRep: Heterogeneous Knowledge Graph based Drug Repositioning
The process of developing new drugs is both time-consuming and costly, often taking over a decade and billions of dollars to obtain regulatory approval. Additionally, the complexity of patent protection for novel compounds presents challenges for pharmaceutical innovation. Drug repositioning offers an alternative strategy to uncover new therapeutic uses for existing medicines. Previous repositioning models have been limited by their reliance on homogeneous data sources, failing to leverage the rich information available in heterogeneous biomedical knowledge graphs. We propose HeteroKGRep, a novel drug repositioning model that utilizes heterogeneous graphs to address these limitations. HeteroKGRep is a multi-step framework that first generates a similarity graph from hierarchical concept relations. It then applies SMOTE over-sampling to address class imbalance before generating node sequences using a heterogeneous graph neural network. Drug and disease embeddings are extracted from the network and used for prediction. We evaluated HeteroKGRep on a graph containing biomedical concepts and relations from ontologies, pathways and literature. It achieved state-of-the-art performance with 99% accuracy, 95% AUC ROC and 94% average precision on predicting repurposing opportunities. Compared to existing homogeneous approaches, HeteroKGRep leverages diverse knowledge sources to enrich representation learning. Based on heterogeneous graphs, HeteroKGRep can discover new drug-disease associations, leveraging de novo drug development. This work establishes a promising new paradigm for knowledge-guided drug repositioning using multimodal biomedical data.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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