Evaluating Switching Neural Networks for Gene Selection

Authors:
Francesca Ruffino;Massimiliano Costacurta;Marco Muselli
Affiliations:
Dipartimento di Scienze dell'Informazione, Università di Milano, Milano, Italy;Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Genova, Italy;Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Genova, Italy
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

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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.