Document Detail

Computational prediction of cancer-gene function.
MedLine Citation:
PMID:  17167517     Owner:  NLM     Status:  MEDLINE    
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.
Pingzhao Hu; Gary Bader; Dennis A Wigle; Andrew Emili
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Review     Date:  2006-12-14
Journal Detail:
Title:  Nature reviews. Cancer     Volume:  7     ISSN:  1474-175X     ISO Abbreviation:  Nat. Rev. Cancer     Publication Date:  2007 Jan 
Date Detail:
Created Date:  2006-12-22     Completed Date:  2007-03-23     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101124168     Medline TA:  Nat Rev Cancer     Country:  England    
Other Details:
Languages:  eng     Pagination:  23-34     Citation Subset:  IM    
Program in Proteomics and Bioinformatics, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.
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MeSH Terms
Computational Biology / methods*
Databases, Genetic
Databases, Protein
Gene Expression Profiling
Gene Expression Regulation, Neoplastic*
Genomics / methods
Models, Biological
Neoplasms / genetics*,  metabolism
Pattern Recognition, Automated
Proteomics / methods

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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