A novel framework to elucidate core classes in a dataset
Soria, Daniele; Garibaldi, Jonathan M.
Jonathan M. Garibaldi
In this paper we present an original framework to extract representative groups from a dataset, and we validate it
over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visualisation techniques are used to characterise the results, and core classes are defined by consensus clustering. Classes may be verified using supervised classification algorithms to obtain a set of rules which may be useful for new data points in the future. This framework is validated over a novel set of histone markers for breast cancer patients. From a technical perspective, the resultant classes are well separated and characterised by low, medium and high levels of biological markers. Clinically, the groups appear to distinguish patients with poor overall survival from those with low grading score and better survival. Overall, this framework offers a promising methodology for elucidating core consensus groups from data.
|Publication Date||Jan 1, 2010|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Soria, D., & Garibaldi, J. M. (2010). A novel framework to elucidate core classes in a dataset|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf|
|Additional Information||Published in: IEEE Congress on Evolutionary Computation (CEC) 2010, IEEE, 2010, ISBN 978-1-4244-8126-2, pp. 1-8
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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