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从 Medline 注释中提取的化学物质、蛋白质和疾病之间的关联挖掘。

Mining connections between chemicals, proteins, and diseases extracted from Medline annotations.

机构信息

School of Information and Library Science, University of North Carolina, Chapel Hill, NC, USA.

出版信息

J Biomed Inform. 2010 Aug;43(4):510-9. doi: 10.1016/j.jbi.2010.03.008. Epub 2010 Mar 27.

Abstract

The biomedical literature is an important source of information about the biological activity and effects of chemicals. We present an application that extracts terms indicating biological activity of chemicals from Medline records, associates them with chemical name and stores the terms in a repository called ChemoText. We describe the construction of ChemoText and then demonstrate its utility in drug research by employing Swanson's ABC discovery paradigm. We reproduce Swanson's discovery of a connection between magnesium and migraine in a novel approach that uses only proteins as the intermediate B terms. We validate our methods by using a cutoff date and evaluate them by calculating precision and recall. In addition to magnesium, we have identified valproic acid and nitric oxide as chemicals which developed links to migraine. We hypothesize, based on protein annotations, that zinc and retinoic acid may play a role in migraine. The ChemoText repository has promise as a data source for drug discovery.

摘要

生物医学文献是了解化学物质生物活性和作用的重要信息源。我们提出了一种从 Medline 记录中提取表示化学物质生物活性的术语的应用程序,将它们与化学名称相关联,并将术语存储在称为 ChemoText 的存储库中。我们描述了 ChemoText 的构建,然后通过使用 Swanson 的 ABC 发现范例演示了它在药物研究中的应用。我们使用仅将蛋白质作为中间 B 术语的新方法重现了 Swanson 发现镁与偏头痛之间的联系。我们使用截止日期验证了我们的方法,并通过计算精度和召回率对其进行了评估。除了镁之外,我们还确定了丙戊酸和一氧化氮是与偏头痛有关的化学物质。我们基于蛋白质注释假设锌和视黄酸可能在偏头痛中发挥作用。ChemoText 存储库有望成为药物发现的数据源。

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本文引用的文献

1
Indexed Pain Journals.
J Pain Palliat Care Pharmacother. 2008;22(1):45-46. doi: 10.1080/15360280801989377.
2
A new evaluation methodology for literature-based discovery systems.
J Biomed Inform. 2009 Aug;42(4):633-43. doi: 10.1016/j.jbi.2008.12.001. Epub 2008 Dec 16.
3
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J Biomed Inform. 2009 Apr;42(2):219-27. doi: 10.1016/j.jbi.2008.08.004. Epub 2008 Aug 19.
5
STITCH: interaction networks of chemicals and proteins.
Nucleic Acids Res. 2008 Jan;36(Database issue):D684-8. doi: 10.1093/nar/gkm795. Epub 2007 Dec 15.
6
DrugBank: a knowledgebase for drugs, drug actions and drug targets.
Nucleic Acids Res. 2008 Jan;36(Database issue):D901-6. doi: 10.1093/nar/gkm958. Epub 2007 Nov 29.
9
CBioC: beyond a prototype for collaborative annotation of molecular interactions from the literature.
Comput Syst Bioinformatics Conf. 2007;6:381-4. doi: 10.1142/9781860948732_0038.
10
Retinoic acid in the development, regeneration and maintenance of the nervous system.
Nat Rev Neurosci. 2007 Oct;8(10):755-65. doi: 10.1038/nrn2212.

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