Abstract
A new gene selection method for analyzing microarray experiments pertaining to two classes of tissues and for determining relevant genes characterizing differences between the two classes is proposed. The new technique is based on Switching Neural Networks (SNN), learning machines that assign a relevance value to each input variable, and adopts Recursive Feature Addition (RFA) for performing gene selection.
The performances of SNN-RFA are evaluated by considering its application on two real and two artificial gene expression datasets generated according to a proper mathematical model that possesses biological and statistical plausibility. Comparisons with other two widely used gene selection methods are also shown.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Golub, T., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Guyon, I., Weston, J., Barnhill, S.: Gene selection for cancer classification using support vectors machines. Machine Learning 46, 389–422 (2002)
Muselli, M.: Switching neural networks: A new connectionist model for classification. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds.) WIRN 2005 and NAIS 2005. LNCS, vol. 3931, pp. 23–30. Springer, Heidelberg (2006)
Muselli, M.: Gene selection through switching neural networks. In: Proceedings of NETTAB-2003: Workshop on Bioinformatics for Microarrays, Bologna, Italy (2003)
Ruffino, F., Muselli, M., Valentini, G.: Gene expression modeling through positive Boolean functions. International Journal of Approximate Reasoning (to appear, 2007)
Alon, U., et al.: Broad patterns of gene expressions revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. In: Proceedings of the National Academy of Science USA 96, pp. 6745–6750 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruffino, F., Costacurta, M., Muselli, M. (2007). Evaluating Switching Neural Networks for Gene Selection. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_71
Download citation
DOI: https://doi.org/10.1007/978-3-540-73400-0_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73399-7
Online ISBN: 978-3-540-73400-0
eBook Packages: Computer ScienceComputer Science (R0)