Computational prediction of cancer-gene function

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

Research output: Contribution to journalReview articlepeer-review

69 Scopus citations


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
Issue number1
StatePublished - Jan 2007

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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