Gene selection using Random Voronoi Ensembles


Francesco Masulli, Stefano Rovetta 


ABSTRACT

In this paper we propose a flexible method for analyzing the relevance
of input variables in high dimensional problems with respect to a given dichotomic
classification problem. Both linear and non-linear cases are considered.
In the linear case, the application of derivative-based saliency yields a commonly
adopted ranking criterion. In the non-linear case, the method is extended by introducing
a resampling technique and by clustering the obtained results for stability
of the estimate. The method was preliminarly validated on the data published by
T.R. Golub et al. on a study, at the molecular level, of two kinds of leukemia:
Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia (Science 5439-
286, 531-537, 1999). Our technique indicates that, among the top 20 genes found
by the final cluster analysis, 8 of the 50 genes listed in the original work feature
a stronger discriminating power.




Pre-Wirn Workshop on Bioinformatcs and Biostatistics,
2003 Workshop Italiano sulle Reti Neurali