Regularized Least Squares Cancer Classifiers from DNA microarray data (Articolo in rivista)

Type
Label
  • Regularized Least Squares Cancer Classifiers from DNA microarray data (Articolo in rivista) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Alternative label
  • N. Ancona, R. Maglietta, A. D’Addabbo, S. Liuni, G. Pesole (2005)
    Regularized Least Squares Cancer Classifiers from DNA microarray data
    in BMC bioinformatics
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • N. Ancona, R. Maglietta, A. D’Addabbo, S. Liuni, G. Pesole (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 6 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1Istituto di Studi sui Sistemi Intelligenti per I'Automazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy 2Istituto di Tecnologie Biomediche-Sezione di Bari, CNR, Via Amendola 122/D, 70126 Bari Italy 3Dipartimento Scienze Biomolecolari e Biotecnologie, Universitá di Milano, Via Caloria 26, 20133 Milano, Italy (literal)
Titolo
  • Regularized Least Squares Cancer Classifiers from DNA microarray data (literal)
Abstract
  • Background 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. Results We show that RLS classifiers have performances comparable to those of SVM classifiers as the Leave-One-Out (LOO) error evaluated on three different data sets shows. The main advantage of RLS machines is that for solving a classification problem they use a linear system of order equal to either the number of features or the number of training examples. Moreover, RLS machines allow to get an exact measure of the LOO error with just one training. Conclusion RLS classifiers are a valuable alternative to SVM classifiers for the problem of cancer classification by gene expression data, due to their simplicity and low computational complexity. Moreover, RLS classifiers show generalization ability comparable to the ones of SVM classifiers also in the case the classification of new specimens involves very few gene expression levels. (literal)
Prodotto di
Autore CNR

Incoming links:


Prodotto
Autore CNR di
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
data.CNR.it