Soria, Daniele, Garibaldi, Jonathan M., Ambrogi, Federico, Green, Andrew R., Powe, Des, Rakha, Emad, Douglas Macmillan, R., Blamey, Roger W., Ball, Graham, Lisboa, Paolo J.G., and others. (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. E-ISSN 1879-0534. (doi:10.1016/j.compbiomed.2010.01.003) (KAR id:98903)
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| Official URL: https://doi.org/10.1016/j.compbiomed.2010.01.003 |
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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. © 2010 Elsevier Ltd. All rights reserved.
| Item Type: | Article |
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| DOI/Identification number: | 10.1016/j.compbiomed.2010.01.003 |
| Subjects: | Q Science > QA Mathematics (inc Computing science) |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Funders: | University of Nottingham (https://ror.org/01ee9ar58) |
| Depositing User: | Daniel Soria |
| Date Deposited: | 08 Dec 2022 15:33 UTC |
| Last Modified: | 12 Dec 2022 13:50 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/98903 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Soria, Daniele.
| Creator's ORCID: |
 https://orcid.org/0000-0002-0164-8218
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| CReDIT Contributor Roles: |
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