http://www.cnr.it/ontology/cnr/individuo/prodotto/ID20599

Evaluating switching neural networks through artificial and real gene expression data (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Evaluating switching neural networks through artificial and real gene expression data (Articolo in rivista) (literal)

Anno

2009-01-01T00:00:00+01:00 (literal)

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

10.1016/j.artmed.2008.08.002 (literal)

Alternative label

M. Muselli, M. Costacurta, F. Ruffino (2009)
Evaluating switching neural networks through artificial and real gene expression data
in Artificial intelligence in medicine (Print); ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, AMSTERDAM (Paesi Bassi)
(literal)

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

M. Muselli, M. Costacurta, F. Ruffino (literal)

Pagina inizio

163 (literal)

Pagina fine

171 (literal)

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

Selected and revised paper Fourth International Meeting on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB, 2007) Portofino Vetta, Ruta di Camogli (Italy), July 2007 (literal)

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

45 (literal)

Rivista

Artificial intelligence in medicine (Rivista)

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

2-3 (literal)

Note

Scopu (literal)
ISI Web of Science (WOS) (literal)

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

F. Ruffino: Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico 39 , 20135 Milano, Italy, M. Costacurta e M. Muselli CNR IEIIT Genova (literal)

Titolo

Evaluating switching neural networks through artificial and real gene expression data (literal)

Abstract

Objective: DNA microarrays offer the possibility of analyzing the expression level for thousands of genes concerning a specific tissue. An important target of this analysis is to derive the subset of genes involved in a biological process of interest. Here, a new promising method for gene selection is proposed, which presents a good level of accuracy and reliability. Methods and materials: The proposed technique adopts switching neural networks (SNN), a particular kind of connectionist models, to assign a relevance value to each gene, thus employing recursive feature addition (RFA) to derive the final list of relevant genes. To fairly evaluate the quality of the new approach, called SNN-RFA, its application on three real and three artificial gene expression datasets, generated according to a proper mathematical model that possesses biological and statistical plausibility, has been considered. In particular, a comparison with other two widely used gene selection methods, namely the signal to noise ratio (S2N) and support vector machines with recursive feature elimination (SVM-RFE), has been performed. Results: In all the considered cases SNN-RFA achieves the best performances, arriving to determine the whole collection of relevant genes in one of the three artificial datasets. The S2N method exhibits a quality similar to that of SNN-RFA, whereas SVM-RFE shows the worst behavior. Conclusion: The quality of the proposed method SNN-RFA has been established together with the usefulness of the mathematical model adopted to generate the artificial datasets of gene expression levels. (literal)

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