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A literature network of human genes for high-throughput analysis of gene expression

A literature network of human genes for high-throughput analysis of gene expression We have carried out automated extraction of explicit and implicit biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for 13,712 named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records. The associations between genes have been annotated by linking genes to terms from the medical subject heading (MeSH) index and terms from the gene ontology (GO) database. The extracted database and accompanying web tools for gene-expression analysis have collectively been named 'PubGene'. We validated the extracted networks by three large-scale experiments showing that co-occurrence reflects biologically meaningful relationships, thus providing an approach to extract and structure known biology. We validated the applicability of the tools by analyzing two publicly available microarray data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Genetics Springer Journals

A literature network of human genes for high-throughput analysis of gene expression

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References (38)

Publisher
Springer Journals
Copyright
Copyright © 2001 by Nature Publishing Group
Subject
Biomedicine; Biomedicine, general; Human Genetics; Cancer Research; Agriculture; Gene Function; Animal Genetics and Genomics
ISSN
1061-4036
eISSN
1546-1718
DOI
10.1038/ng0501-21
Publisher site
See Article on Publisher Site

Abstract

We have carried out automated extraction of explicit and implicit biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for 13,712 named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records. The associations between genes have been annotated by linking genes to terms from the medical subject heading (MeSH) index and terms from the gene ontology (GO) database. The extracted database and accompanying web tools for gene-expression analysis have collectively been named 'PubGene'. We validated the extracted networks by three large-scale experiments showing that co-occurrence reflects biologically meaningful relationships, thus providing an approach to extract and structure known biology. We validated the applicability of the tools by analyzing two publicly available microarray data sets.

Journal

Nature GeneticsSpringer Journals

Published: May 1, 2001

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