Linked Life Data

17167517

Source:http://linkedlifedata.com/resource/pubmed/id/17167517

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-12-22
pubmed:abstractText
Most cancer genes remain functionally uncharacterized in the physiological context of disease development. High-throughput molecular profiling and interaction studies are increasingly being used to identify clusters of functionally linked gene products related to neoplastic cell processes. However, in vivo determination of cancer-gene function is laborious and inefficient, so accurately predicting cancer-gene function is a significant challenge for oncologists and computational biologists alike. How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets? We explore plausible solutions to this important challenge.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1474-175X
pubmed:author
pubmed:issnType
Print
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
23-34
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Computational prediction of cancer-gene function.
pubmed:affiliation
Program in Proteomics and Bioinformatics, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't