Suppr超能文献

支持药物发现的文献挖掘。

Literature mining in support of drug discovery.

作者信息

Agarwal Pankaj, Searls David B

机构信息

GlaxoSmithKline R&D, 709 Swedeland Road, UW2230, King of Prussia, PA 19406.

出版信息

Brief Bioinform. 2008 Nov;9(6):479-92. doi: 10.1093/bib/bbn035. Epub 2008 Sep 27.

Abstract

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.

摘要

药物发现领域为数据整合提供了强大的驱动力。虽然该领域的注意力往往集中在整合来自组学和其他有助于药物发现与开发的大型平台技术的原始数据,但科学文献仍然是对制药企业具有重要价值的信息来源,因此挖掘此类数据并将其与其他来源进行整合的工具具有至关重要的意义和经济影响。本综述简要概述了与药物发现相关的文献挖掘方法,并提供了一个我们在工业环境中实施的“轻量级”方法的实例研究。

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