A novel algorithm for analyzing drug-drug interactions from MEDLINE literature.
A novel algorithm for analyzing drug-drug interactions from MEDLINE literature.
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DOI：
10.1038/srep17357
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发表时间：
2015-11-27
影响因子：
4.6
通讯作者：
Cheng F
中科院分区：
文献类型：
--
作者：
Lu Y;Shen D;Pietsch M;Nagar C;Fadli Z;Huang H;Tu YC;Cheng F
药物相互作用（DDI）正在成为一个严重的临床安全性问题，因为多种药物的使用变得越来越普遍。在MEDLINE数据库中搜索与DDI相关的期刊文章，产生了超过330，000个结果。手动阅读和总结这些参考文献是不可能的。随着MEDLINE数据库中生物医学参考文献的数量持续快速增长，从文献中自动识别DDI变得越来越重要。在这篇文章中，我们提出了一个基于随机抽样的统计算法，以确定可能的DDI和潜在的机制，从物质领域的MEDLINE记录。物质术语本质上是MEDLINE记录中化合物（包括蛋白质）信息的载体。对华法林、布洛芬、呋塞米和舍曲林的四项病例研究表明，我们的方法能够以较高的准确度对可能的DDI进行排序（华法林为90.0%，布洛芬为83.3%，呋塞米为70.0%，舍曲林为100%，按p值排序的化合物列表中）。还进行了物质术语的社会网络分析，以构建蛋白质和药物对之间的网络，以阐明两种药物如何相互作用。
Drug–drug interaction (DDI) is becoming a serious clinical safety issue as the use of multiple medications becomes more common. Searching the MEDLINE database for journal articles related to DDI produces over 330,000 results. It is impossible to read and summarize these references manually. As the volume of biomedical reference in the MEDLINE database continues to expand at a rapid pace, automatic identification of DDIs from literature is becoming increasingly important. In this article, we present a random-sampling-based statistical algorithm to identify possible DDIs and the underlying mechanism from the substances field of MEDLINE records. The substances terms are essentially carriers of compound (including protein) information in a MEDLINE record. Four case studies on warfarin, ibuprofen, furosemide and sertraline implied that our method was able to rank possible DDIs with high accuracy (90.0% for warfarin, 83.3% for ibuprofen, 70.0% for furosemide and 100% for sertraline in the top 10% of a list of compounds ranked by p-value). A social network analysis of substance terms was also performed to construct networks between proteins and drug pairs to elucidate how the two drugs could interact.