http://www.cnr.it/ontology/cnr/individuo/prodotto/ID72363

Regularized least squares cancer classifiers from DNA microarray data (Articolo in rivista)

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Regularized least squares cancer classifiers from DNA microarray data (Articolo in rivista) (literal)

Anno

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

Alternative label

Ancona N., Maglietta R., D'Addabbo A. Liuni S., Mignone F., Talamo I.G. and Pesole G. (2005)
Regularized least squares cancer classifiers from DNA microarray data
in BMC bioinformatics
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Ancona N., Maglietta R., D'Addabbo A. Liuni S., Mignone F., Talamo I.G. and Pesole G. (literal)

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Istituto di Tecnologie Biomediche,CNR -Sede di Bari Istituto di Studi sui Sistemi Intelligenti per l'Automazione,CNR Dipartimento Scienze Biomolecolari e Biotecnologie, Università di Milano (literal)

Titolo

Abstract

The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information provided by DNA microarrays allows an approach to the problem of cancer analysis from a quantitative rather than qualitative point of view. Cancer classification requires well founded mathematical methods which are able to predict the status of new specimens with high significance levels starting from a limited number of data. In this paper we assess the performances of Regularized Least Squares (RLS) classifiers, originally proposed in regularization theory, by comparing them with Support Vector Machines (SVM), the state-of-the-art supervised learning technique for cancer classification by DNA microarray data. The performances of both approaches have been also investigated with respect to the number of selected genes and different gene selection strategies. (literal)

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