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挖掘生物医学知识的专利文本。

Text mining patents for biomedical knowledge.

作者信息

Rodriguez-Esteban Raul, Bundschus Markus

机构信息

Roche Pharmaceutical Research and Early Development, pRED Informatics, Roche Innovation Center Basel, 4070 Basel, Switzerland.

Scientific & Business Information Services, Roche Diagnostics GmbH, 82377 Penzberg, Germany.

出版信息

Drug Discov Today. 2016 Jun;21(6):997-1002. doi: 10.1016/j.drudis.2016.05.002. Epub 2016 May 11.

Abstract

Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery.

摘要

对诸如医学文献数据库(Medline)等科学知识库进行生物医学文本挖掘,近年来备受关注。鉴于文本挖掘能够从非结构化文本来源中自动提取围绕基因、蛋白质和药物等实体的生物医学事实,它被视为促进生物医学研究和药物发现的主要推动因素。与生物医学文献不同,对生物医学专利挖掘的研究尚未达到相同的成熟水平。在此,我们回顾现有工作,并突出从专利中自动提取事实所带来的相关技术挑战。最后,我们概述该领域未来可能的发展方向,这些方向有助于推动生物医学研究和药物发现。

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