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A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

Soria, Daniele; Garibaldi, Jonathan M.; Ambrogi, Federico; Green, Andrew R.; Powe, Des; Rakha, Emad; Douglas Macmillan, R.; Blamey, Roger W.; Ball, Graham; Lisboa, Paulo J.G.; Etchells, Terence A.; Boracchi, Patrizia; Biganzoli, Elia M.; Ellis, Ian O.

Authors

Daniele Soria

Jonathan M. Garibaldi

Federico Ambrogi

Andrew R. Green

Des Powe

EMAD RAKHA Emad.Rakha@nottingham.ac.uk
Professor of Breast Cancer Pathology

R. Douglas Macmillan

Roger W. Blamey

Graham Ball

Paulo J.G. Lisboa

Terence A. Etchells

Patrizia Boracchi

Elia M. Biganzoli

Ian O. Ellis



Abstract

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’ by using a range of techniques to reach consensus across several different clustering algorithms, and to ascertain the key characteristics of these classes. We apply the methodology to immunohistochemical data from breast cancer patients. In doing so, we identify six core classes, of which several may be novel sub-groups not previously emphasised in literature.

Journal Article Type Article
Publication Date Mar 1, 2010
Journal Computers in biology and medicine
Print ISSN 0010-4825
Electronic ISSN 0010-4825
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 40
Issue 3
APA6 Citation Soria, D., Garibaldi, J. M., Ambrogi, F., Green, A. R., Powe, D., Rakha, E., …Ellis, I. O. (2010). A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Computers in Biology and Medicine, 40(3), doi:10.1016/j.compbiomed.2010.01.003
DOI https://doi.org/10.1016/j.compbiomed.2010.01.003
Publisher URL http://www.sciencedirect.com/science/article/pii/S0010482510000053
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information NOTICE: this is the author’s version of a work that was accepted for publication in Computers in Biology and Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers in Biology and Medicine, 40(3), 2010. doi: 10.1016/j.compbiomed.2010.01.003

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf







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