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_name
Nat Genetjournal_title
Nature geneticsauthors
Jenssen TK,Laegreid A,Komorowski J,Hovig Edoi
10.1038/ng0501-21keywords:
subject
Has Abstractpub_date
2001-05-01 00:00:00pages
21-8issue
1eissn
1061-4036issn
1546-1718pii
88213journal_volume
28pub_type
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