Computational prediction of cancer-gene function

Pingzhao Hu, Gary Bader, Dennis A Wigle, Andrew Emili

Research output: Contribution to journalArticle

65 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)23-34
Number of pages12
JournalNature Reviews Cancer
Volume7
Issue number1
DOIs
StatePublished - Jan 2007

Fingerprint

Neoplasm Genes
Neoplastic Processes
Proteomics
Genes

ASJC Scopus subject areas

  • Cancer Research

Cite this

Computational prediction of cancer-gene function. / Hu, Pingzhao; Bader, Gary; Wigle, Dennis A; Emili, Andrew.

In: Nature Reviews Cancer, Vol. 7, No. 1, 01.2007, p. 23-34.

Research output: Contribution to journalArticle

Hu, Pingzhao ; Bader, Gary ; Wigle, Dennis A ; Emili, Andrew. / Computational prediction of cancer-gene function. In: Nature Reviews Cancer. 2007 ; Vol. 7, No. 1. pp. 23-34.
@article{3f5c813a2d4b410595476534df0f6f5f,
title = "Computational prediction of cancer-gene function",
abstract = "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.",
author = "Pingzhao Hu and Gary Bader and Wigle, {Dennis A} and Andrew Emili",
year = "2007",
month = "1",
doi = "10.1038/nrc2036",
language = "English (US)",
volume = "7",
pages = "23--34",
journal = "Nature Reviews Cancer",
issn = "1474-175X",
publisher = "Nature Publishing Group",
number = "1",

}

TY - JOUR

T1 - Computational prediction of cancer-gene function

AU - Hu, Pingzhao

AU - Bader, Gary

AU - Wigle, Dennis A

AU - Emili, Andrew

PY - 2007/1

Y1 - 2007/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33845864703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33845864703&partnerID=8YFLogxK

U2 - 10.1038/nrc2036

DO - 10.1038/nrc2036

M3 - Article

C2 - 17167517

AN - SCOPUS:33845864703

VL - 7

SP - 23

EP - 34

JO - Nature Reviews Cancer

JF - Nature Reviews Cancer

SN - 1474-175X

IS - 1

ER -