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International Conference on Algorithms for Computational Biology

AlCoB 2017: Algorithms for Computational Biology pp 3–23Cite as

Biomedical Applications of Prototype Based Classifiers and Relevance Learning

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10252))

Abstract

In this contribution, prototype-based systems and relevance learning are presented and discussed in the context of biomedical data analysis. Learning Vector Quantization and Matrix Relevance Learning serve as the main examples. After introducing basic concepts and related approaches, example applications of Generalized Matrix Relevance Learning are reviewed, including the classification of adrenal tumors based on steroid metabolomics data, the analysis of cytokine expression in the context of Rheumatoid Arthritis, and the prediction of recurrence risk in renal tumors based on gene expression.

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Acknowledgments

The author would like to thank the collaboration partners and co-authors of the publications which are reviewed in this contribution or could be mentioned only briefly.

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Authors and Affiliations

  1. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, P.O. Box 407, 9700 AK, Groningen, The Netherlands

    Michael Biehl

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  1. Michael Biehl
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Correspondence to Michael Biehl .

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Editors and Affiliations

  1. University of Aveiro, Aveiro, Portugal

    Daniel Figueiredo

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. University of Aveiro, Aveiro, Portugal

    Diogo Pratas

  4. University of Extremadura, Caceres, Spain

    Miguel A. Vega-Rodríguez

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Biehl, M. (2017). Biomedical Applications of Prototype Based Classifiers and Relevance Learning. In: Figueiredo, D., Martín-Vide, C., Pratas, D., Vega-Rodríguez, M. (eds) Algorithms for Computational Biology. AlCoB 2017. Lecture Notes in Computer Science(), vol 10252. Springer, Cham. https://doi.org/10.1007/978-3-319-58163-7_1

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