Text-mining solutions for biomedical research: enabling integrative biology

被引：149

作者：

Rebholz-Schuhmann, Dietrich ^{[1
,2
]}

Oellrich, Anika ^{[1
]}

Hoehndorf, Robert ^{[3
,4
]}

机构：

[1] European Bioinformat Inst, Cambridge CB10 1SD, England

[2] Univ Zurich, Inst Comp Linguist, CH-8050 Zurich, Switzerland

[3] Univ Cambridge, Dept Genet, Cambridge CB2 3EH, England

[4] Univ Cambridge, Dept Physiol Dev & Neurosci, Cambridge CB2 3EG, England

来源：

NATURE REVIEWS GENETICS | 2012年 / 13卷 / 12期

关键词：

PROTEIN-PROTEIN INTERACTIONS; KNOWLEDGE; INFORMATION; GENES; IDENTIFICATION; DISCOVERY; NETWORKS; TOOL; NORMALIZATION; RECOGNITION;

D O I：

10.1038/nrg3337

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

In response to the unbridled growth of information in literature and biomedical databases, researchers require efficient means of handling and extracting information. As well as providing background information for research, scientific publications can be processed to transform textual information into database content or complex networks and can be integrated with existing knowledge resources to suggest novel hypotheses. Information extraction and text data analysis can be particularly relevant and helpful in genetics and biomedical research, in which up-to-date information about complex processes involving genes, proteins and phenotypes is crucial. Here we explore the latest advancements in automated literature analysis and its contribution to innovative research approaches.

引用

页码：829 / 839

页数：11

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