Identification of high-quality cancer prognostic markers and metastasis network modules

Jie Li, Anne E.G. Lenferink, Yinghai Deng, Catherine Collins, Qinghua Cui, Enrico O. Purisima, Maureen D. O'Connor-McCourt and Edwin Wang ()
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Jie Li: Computational Chemistry and Bioinformatics Group, Biotechnology Research Institute, National Research Council Canada
Anne E.G. Lenferink: Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada
Yinghai Deng: Computational Chemistry and Bioinformatics Group, Biotechnology Research Institute, National Research Council Canada
Catherine Collins: Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada
Qinghua Cui: Computational Chemistry and Bioinformatics Group, Biotechnology Research Institute, National Research Council Canada
Enrico O. Purisima: Computational Chemistry and Bioinformatics Group, Biotechnology Research Institute, National Research Council Canada
Maureen D. O'Connor-McCourt: Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada
Edwin Wang: Computational Chemistry and Bioinformatics Group, Biotechnology Research Institute, National Research Council Canada

Nature Communications, 2010, vol. 1, issue 1, 1-9

Abstract: There has been great interest in attempting to identify gene expression signatures that predict cancer survival. In this study a new algorithm is developed to analyse gene expression datasets that accurately classify both ER+ and ER− breast cancers into low- and high-risk groups.

Date: 2010
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DOI: 10.1038/ncomms1033

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