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  Gene selection via the BAHSIC family of algorithms

Song, L., Bedo, J., Borgwardt, K., Gretton, A., & Smola, A. (2007). Gene selection via the BAHSIC family of algorithms. Bioinformatics, 23(13: ISMB/ECCB 2007 Conference Proceedings), i490-i498.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CC9D-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-E128-2
Genre: Conference Paper

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 Creators:
Song, L, Author
Bedo, J, Author
Borgwardt, KM, Author              
Gretton, A1, 2, Author              
Smola, A, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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 non-linear kernels can be more suitable.

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 Dates: 2007-07
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btm216
BibTex Citekey: 4764
 Degree: -

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Title: 15th Annual International Conference on Intelligent Systems for Molecular Biology & 6th European Conference on Computational Biology (ISMB/ECCB 2007)
Place of Event: Wien, Austria
Start-/End Date: 2007-07-21 - 2007-07-25

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 23 (13: ISMB/ECCB 2007 Conference Proceedings) Sequence Number: - Start / End Page: i490 - i498 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991