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Signature Selection for Grouped Features with a Case Study on Exon Microarrays

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Feature Selection for Data and Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 584))

Abstract

When features are grouped, it is desirable to perform feature selection groupwise in addition to selecting individual features. It is typically the case in data obtained by modern high-throughput genomic profiling technologies such as exon microarrays, which measure the amount of gene expression in fine resolution. Exons are disjoint subsequences corresponding to coding regions in genes, and exon microarrays enable us to study the event of different usage of exons, called alternative splicing, which is presumed to contribute to development of diseases. To identify such events, all exons that belong to a relevant gene may have to be selected, perhaps with different weights assigned to them to detect most relevant ones. In this chapter we discuss feature selection methods to handle grouped features. A popular shrinkage method, lasso, and its variants will be our focus, that are based on regularized regression with generalized linear models. Data from exon microarrays will be used for a case study.

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Notes

  1. 1.

    http://www.geneontology.org

  2. 2.

    http://www.affymetrix.com

  3. 3.

    http://www.ncbi.nlm.nih.gov/geo

  4. 4.

    http://www.affymetrix.com/analysis/downloads/na33/

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Acknowledgments

This work has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis”, project C1.

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Authors and Affiliations

  1. Technische Universität Dortmund, Joseph-von-Fraunhofer-Strasse 23, 44227, Dortmund, Germany

    Sangkyun Lee

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  1. Sangkyun Lee
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Correspondence to Sangkyun Lee .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  2. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Lee, S. (2015). Signature Selection for Grouped Features with a Case Study on Exon Microarrays. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_14

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  • DOI: https://doi.org/10.1007/978-3-662-45620-0_14

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