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.
A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients / D. Soria, J.M. Garibaldi, F. Ambrogi, A.R. Green, D. Powe, E. Rakha, R.D. Macmillan, R.W. Blamey, G. Ball, P.J. Lisboa, T.A. Etchells, P. Boracchi, E. Biganzoli, I.O. Ellis. - In: COMPUTERS IN BIOLOGY AND MEDICINE. - ISSN 0010-4825. - 40:3(2010), pp. 318-330.
|Titolo:||A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients|
BIGANZOLI, ELIA (Penultimo)
|Parole Chiave:||Breast cancer; Clustering methods; Consensus clustering; Molecular classification; Validity indices|
|Settore Scientifico Disciplinare:||Settore MED/01 - Statistica Medica|
Settore INF/01 - Informatica
|Data di pubblicazione:||2010|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.compbiomed.2010.01.003|
|Appare nelle tipologie:||01 - Articolo su periodico|