Details

Autor(en) / Beteiligte

• 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
• Ellis, Ian O

Titel

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

Ist Teil von

• Computers in biology and medicine, 2010-03, Vol.40 (3), p.318-330

Ort / Verlag

United States: Elsevier Ltd

Erscheinungsjahr

2010

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.