Variable selection using svm based criteria
The Journal of Machine Learning Research
Analysis of variance components in gene expression data
Bioinformatics
Gene selection for multiclass prediction by weighted fisher criterion
EURASIP Journal on Bioinformatics and Systems Biology
Gene expression modeling through positive boolean functions
International Journal of Approximate Reasoning
Gene selection via the BAHSIC family of algorithms
Bioinformatics
Switching neural networks: a new connectionist model for classification
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Guest editorial: Computational intelligence and machine learning in bioinformatics
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
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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.