A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients
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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
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Elsevier
2010
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| Online Access: | https://eprints.nottingham.ac.uk/28133/ |
| _version_ | 1848793514901504000 |
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| author | 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. |
| author_facet | 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. |
| author_sort | Soria, Daniele |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:01:31Z |
| format | Article |
| id | nottingham-28133 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:31Z |
| publishDate | 2010 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-281332020-05-04T20:25:09Z https://eprints.nottingham.ac.uk/28133/ 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. 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. Elsevier 2010-03 Article PeerReviewed 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 http://www.sciencedirect.com/science/article/pii/S0010482510000053 doi:10.1016/j.compbiomed.2010.01.003 doi:10.1016/j.compbiomed.2010.01.003 |
| spellingShingle | 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. A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients |
| title | A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients |
| title_full | A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients |
| title_fullStr | A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients |
| title_full_unstemmed | A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients |
| title_short | A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients |
| title_sort | methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients |
| url | https://eprints.nottingham.ac.uk/28133/ https://eprints.nottingham.ac.uk/28133/ https://eprints.nottingham.ac.uk/28133/ |