Linked Life Data

11301300

Source:http://linkedlifedata.com/resource/pubmed/id/11301300

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2001-4-13
pubmed:abstractText
MOTIVATION: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. RESULTS: We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
319-26
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Use of keyword hierarchies to interpret gene expression patterns.
pubmed:affiliation
Department of Medicine, UCSD Cancer Center, University of California, San Diego, San Diego, CA 92093, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.