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

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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. and Ellis, Ian 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). pp. 318-330. ISSN 0010-4825

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Official URL: http://www.sciencedirect.com/science/article/pii/S0010482510000053

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.

Item Type:	Article
RIS ID:	https://nottingham-repository.worktribe.com/output/1012138
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
Schools/Departments:	University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:	10.1016/j.compbiomed.2010.01.003
Depositing User:	Soria, Dr Daniele
Date Deposited:	30 Jan 2015 16:27
Last Modified:	04 May 2020 20:25
URI:	https://eprints.nottingham.ac.uk/id/eprint/28133

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