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

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

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (8MB) | Preview

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
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 Campus > Faculty of Medicine and Health Sciences > School of Medicine
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.compbiomed.2010.01.003
Depositing User: Soria, Dr Daniele
Date Deposited: 30 Jan 2015 16:27
Last Modified: 20 Sep 2016 03:25
URI: http://eprints.nottingham.ac.uk/id/eprint/28133

Actions (Archive Staff Only)

Edit View Edit View