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

作者：Soria Daniele^*; Garibaldi Jonathan M; Ambrogi Federico; Green Andrew R; Powe Des; Rakha Emad; Macmillan R Douglas; Blamey Roger W; Ball Graham; Li**oa Paulo J G; Etchells Terence A; Boracchi Patrizia; Biganzoli Elia; Ellis Ian O

来源：Computers in Biology and Medicine, 2010, 40(3): 318-330.

DOI：10.1016/j.compbiomed.2010.01.003

• Breast cancer
• Molecular classification
• Clustering methods
• Consensus clustering
• Validity indices

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.

• 出版日期2010-3
• 单位河北医科大学