Gene Selection via the BAHSIC Family of Algorithms (2007)
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| Venue: | BIOINFORMATICS |
| Citations: | 10 - 2 self |
BibTeX
@MISC{Song07geneselection,
author = {Le Song and Justin Bedo and Karsten M. Borgwardt and Arthur Gretton and Alex Smola},
title = { Gene Selection via the BAHSIC Family of Algorithms},
year = {2007}
}
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Abstract
Motivation Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are involved in disease outbreak and progression. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods. To accomplish biologically meaningful gene selection from microarray data, we have to understand the theoretical connections and the differences between these methods. In this article, we define a kernel-based framework for feature selection based on the Hilbert-Schmidt Independence Criterion and backward elimination, called BAHSIC. We show that several well-known feature selectors are instances of BAHSIC, thereby clarifying their relationship. Furthermore, by choosing a different kernel, BAHSIC allows us to easily define novel feature selection algorithms. As a further advantage, feature selection via BAHSIC works directly on multiclass problems. Results In a broad experimental evaluation, the members of the BAHSIC family reach high levels of accuracy and robustness when compared to other feature selection techniques. Experiments show that features selected with a linear kernel provide the best classification performance in general, but if strong non-linearities are present in the data then nonlinear kernels can be more suitable. Availability: Accompanying homepage is
Keyphrases
bahsic family gene selection feature selection multiclass problem ultimate goal classification performance broad experimental evaluation hilbert-schmidt independence criterion backward elimination motivation identifying significant gene different method kernel-based framework central challenge disease outbreak novel feature selection cancer research gene list several well-known feature selector feature selection technique high level linear kernel accompanying homepage strong non-linearities microarray data meaningful gene selection different kernel theoretical connection
