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Text-mining solutions for biomedical research: enabling integrative biology


Rebholz-Schuhmann, Dietrich; Oellrich, Anika; Hoehndorf, Robert (2012). Text-mining solutions for biomedical research: enabling integrative biology. Nature Reviews. Genetics, 13(12):829-839.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2012
Deposited On:17 Dec 2012 16:24
Last Modified:17 Feb 2018 00:15
Publisher:Nature Publishing Group
ISSN:1471-0056
OA Status:Closed
Publisher DOI:https://doi.org/10.1038/nrg3337
PubMed ID:23150036

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