Comput. Biol. Med. - A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients.

Tópicos

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Resumo

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.

Resumo Limpo

singl cluster method often use elucid cluster high dimension medic data even though relianc singl algorithm known problemat paper present methodolog determin set core class use rang techniqu reach consensus across sever differ cluster algorithm ascertain key characterist class appli methodolog immunohistochem data breast cancer patient identifi six core class sever may novel subgroup previous emphasis literatur

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