挖掘生物医学文献以探索癌症药物与膳食补充剂之间的相互作用。
Mining Biomedical Literature to Explore Interactions between Cancer Drugs and Dietary Supplements.
作者信息
Zhang Rui, Adam Terrance J, Simon Gyorgy, Cairelli Michael J, Rindflesch Thomas, Pakhomov Serguei, Melton Genevieve B
机构信息
Institute for Health Informatics, University of Minnesota, Minneapolis, MN ; Department of Surgery, University of Minnesota, Minneapolis, MN.
Institute for Health Informatics, University of Minnesota, Minneapolis, MN ; College of Pharmacy, University of Minnesota, Minneapolis, MN.
出版信息
AMIA Jt Summits Transl Sci Proc. 2015 Mar 23;2015:69-73. eCollection 2015.
Interactions between cancer drugs and dietary supplements are clinically important and have not been extensively investigated through mining of the biomedical literature. We report on a previously introduced method now enhanced by machine learning-based filtering. Potential interactions are extracted by using relationships in the form of semantic predications. Semantic predications stored in SemMedDB, a database of structured knowledge generated from MEDLINE, were filtered and connected by two interaction pathways to explore potential drug-supplement interactions (DSIs). The lasso regression filter was trained by using SemRep output features in an expert annotated corpus and used to rank retrieved predications by predicted precision. We found not only known interactions but also inferred several unknown potential DSIs by appropriate filtering and linking of semantic predications.
癌症药物与膳食补充剂之间的相互作用在临床上具有重要意义，且尚未通过挖掘生物医学文献进行广泛研究。我们报告了一种先前引入的方法，该方法现在通过基于机器学习的过滤得到了增强。潜在的相互作用通过以语义谓词形式的关系来提取。存储在SemMedDB（一个从MEDLINE生成的结构化知识数据库）中的语义谓词通过两条相互作用途径进行过滤和连接，以探索潜在的药物 - 补充剂相互作用（DSIs）。套索回归过滤器在专家注释语料库中使用SemRep输出特征进行训练，并用于根据预测精度对检索到的谓词进行排名。我们不仅发现了已知的相互作用，还通过对语义谓词进行适当的过滤和链接推断出了几种未知的潜在DSIs。
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