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