Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case
Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case
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DOI：
10.1109/ickg55886.2022.00018
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
A. Hamed;Jakub Jończyk;Mohammad Zaiyan Alam;E. Deelman;Byung Suk Lee
中科院分区：
文献类型：
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作者：
A. Hamed;Jakub Jończyk;Mohammad Zaiyan Alam;E. Deelman;Byung Suk Lee
本文提出了一种计算方法，旨在构建和查询一个基于文献的知识图预测新药治疗。其主要目标是提供一个平台，从FDA批准的药物中发现药物组合，并加速领域科学家的研究。具体而言，本文介绍了以下算法：（1）从生物医学文献中提到的药物，基因和疾病构建知识图的算法;（2）从可能造成药物相互作用风险的药物组合中审查知识图的算法;（3）以及通过单一药物或药物组合搜索知识图的两种查询算法。最终的知识图谱包含844种药物、306个基因/蛋白质特征和19种疾病。最初生成的药物组合数为2 001。我们查询了知识图谱，以消除非药物化学物质产生的噪音。这一步骤产生了614种药物组合。当审查知识图谱以消除潜在风险的药物组合时，它预测了200种组合。我们的领域专家手动排除了额外的54个组合，只剩下146个候选组合。我们的三层知识图，由我们的算法授权，为科学家提供了一种预测药物组合疗法的工具，他们可以从药物靶点和副作用的角度进一步研究。
This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.