Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.
Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.
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DOI：
10.1093/bioinformatics/btq382
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发表时间：
2010-09-15
期刊：
影响因子：
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通讯作者：
Baral C
中科院分区：
文献类型：
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作者：
Tari L;Anwar S;Liang S;Cai J;Baral C
动机：确定药物-药物相互作用(DDIS)是药物管理和药物开发中的一个关键过程。临床支持工具通常提供全面的DDIS列表，但它们通常缺乏支持的科学证据，不同的工具可能会返回不一致的结果。在这篇文章中，我们提出了一种新的方法，结合文本挖掘和自动推理来获得DDIS。通过提取药物代谢的各种事实，不仅可以提取文本中明确提到的DDIS，而且可以通过推理推断出潜在的相互作用。结果：我们的方法能够找到DrugBank中没有的几个潜在的DDis。我们根据支持证据对这些交互进行了手动评估，我们的分析显示，81.3%的交互被确定为正确的。这表明我们的方法可以用科学证据来解释相互作用的机制，从而发现潜在的DDIS。联系人：luis.tari@roche.com
Motivation: Identifying drug–drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning. Results: Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions. Contact: luis.tari@roche.com