Discovery and Explanation of Drug-Drug Interactions via Text Mining
Discovery and Explanation of Drug-Drug Interactions via Text Mining
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DOI：
10.1142/9789814366496_0040
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发表时间：
2011-12
影响因子：
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通讯作者：
B. Percha;Yael Garten;R. Altman
中科院分区：
文献类型：
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作者：
B. Percha;Yael Garten;R. Altman
当两种药物与同一基因产物相互作用时，就会发生药物-药物相互作用(DDIS)。关于基因-药物关系的大多数可用信息都包含在科学文献中，但分散在大量出版物中，每月新增数千份出版物。在这种情况下，自动文本挖掘是一个有吸引力的解决方案，用于识别基因与药物的关系，并将它们聚集在一起预测新的DDiS。在以前的工作中，我们已经证明可以高保真地从Medline摘要中提取基因-药物相互作用-我们不仅提取基因和药物，而且还提取单个句子中表达的关系类型(例如，代谢、抑制、激活等)。我们将这些关系标准化，并将它们映射到标准化的本体。在这项工作中，我们假设我们可以结合这些标准化的基因-药物关系，从非常广泛和多样化的文献中提取，以推断DDIS。使用建立的DDIS的训练集，我们已经训练了一个随机森林分类器来根据从文献中提取的归一化断言的特征来给潜在的DDIS评分，这些归一化断言涉及两种药物与基因产品。该分类器识别与黄金标准DDIS最相关的关系、药物和基因的组合，正确识别与相互作用的药物对有关的断言的79.8%，与非相互作用的药物对有关的断言的78.9%。最重要的是，因为我们的文本处理方法捕获了单个基因-药物关系的语义，所以我们可以为新提出的DDIS构建机械性的药理学解释。我们展示了如何使用我们的分类器来解释已知的DDI和发现尚未报告的新的DDI。
Drug-drug interactions (DDIs) can occur when two drugs interact with the same gene product. Most available information about gene-drug relationships is contained within the scientific literature, but is dispersed over a large number of publications, with thousands of new publications added each month. In this setting, automated text mining is an attractive solution for identifying gene-drug relationships and aggregating them to predict novel DDIs. In previous work, we have shown that gene-drug interactions can be extracted from Medline abstracts with high fidelity - we extract not only the genes and drugs, but also the type of relationship expressed in individual sentences (e.g. metabolize, inhibit, activate and many others). We normalize these relationships and map them to a standardized ontology. In this work, we hypothesize that we can combine these normalized gene-drug relationships, drawn from a very broad and diverse literature, to infer DDIs. Using a training set of established DDIs, we have trained a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product. The classifier recognizes the combinations of relationships, drugs and genes that are most associated with the gold standard DDIs, correctly identifying 79.8% of assertions relating interacting drug pairs and 78.9% of assertions relating noninteracting drug pairs. Most significantly, because our text processing method captures the semantics of individual gene-drug relationships, we can construct mechanistic pharmacological explanations for the newly-proposed DDIs. We show how our classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported.