کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
505866 | 864543 | 2010 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 40, Issue 3, March 2010, Pages 318–330
Journal: Computers in Biology and Medicine - Volume 40, Issue 3, March 2010, Pages 318–330
نویسندگان
Daniele Soria, Jonathan M. Garibaldi, Federico Ambrogi, Andrew R. Green, Des Powe, Emad Rakha, R. Douglas Macmillan, Roger W. Blamey, Graham Ball, Paulo J.G. Lisboa, Terence A. Etchells, Patrizia Boracchi, Elia Biganzoli, Ian O. Ellis,