Evaluating switching neural networks through artificial and real gene expression data

Summary 

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.

PACS: 87.17.d, 87.80.y

Keywords: Gene selection, Machine learning, Switching neural networks, Recursive feature addition, Shadow clustering