Evaluating switching neural networks through artificial and real gene expression data.
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
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.


CONCLUSIONS
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.
Muselli
Marco
M
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, via De Marini 6, 16149 Genova, Italy. marco.muselli@ieiit.cnr.it
Costacurta
Massimiliano
M
Ruffino
Francesca
F
D015870:Gene Expression; D008962:Models, Theoretical; D016571:Neural Networks, Computer