A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer
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* Corresponding author: Andrew E Teschendorff aet21@cam.ac.uk
Breast Cancer Research 2008, 10:R73 doi:10.1186/bcr2138
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BioMed Central: 6 citations
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Pierre-Emmanuel Colombo, Fernanda Milanezi, Britta Weigelt, Jorge S Reis-Filho Breast Cancer Research 2011, 13:212 (27 June 2011) In this review, the authors examine the clinical relevance of microarray-based profiling of breast cancer and discuss its impact on patient management.
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Prognostic gene network modules in breast cancer hold promise Andrew E Teschendorff, Yan Jiao, Carlos Caldas Breast Cancer Research 2010, 12:317 (8 December 2010) |
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Increased entropy of signal transduction in the cancer metastasis phenotype Andrew E Teschendorff, Simone Severini BMC Systems Biology 2010, 4:104 (30 July 2010) Metastatic breast cancer is associated with a higher degree of randomness in signal transduction patterns and can be identified by measuring the entropy within integrated protein interaction mRNA expression networks.
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A fuzzy gene expression-based computational approach improves breast cancer prognostication Benjamin Haibe-Kains, Christine Desmedt, Françoise Rothé, Martine Piccart, Christos Sotiriou, Gianluca Bontempi Genome Biology 2010, 11:R18 (15 February 2010) A fuzzy computational approach that takes into account several molecular subtypes in order to provide more accurate breast cancer prognosis |
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Intrinsic bias in breast cancer gene expression data sets Jonathan D Mosley, Ruth A Keri BMC Cancer 2009, 9:214 (29 June 2009) |