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Kernel Methods in Bioinformatics

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Kernel methods have now witnessed more than a decade of increasing popularity in the bioinformatics community. In this article, we will compactly review this development, examining the areas in which kernel methods have contributed to computational biology and describing the reasons for their success.

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Notes

  1. 1.

    The machine learning community often (incorrectly) uses the term positive definite rather than positive semi-definite.

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  1. Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, Tübingen, Spemannstr. 38, Tübingen, Germany

    Karsten M. Borgwardt

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  1. Karsten M. Borgwardt
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Correspondence to Karsten M. Borgwardt .

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  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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© 2011 Springer-Verlag Berlin Heidelberg

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Borgwardt, K.M. (2011). Kernel Methods in Bioinformatics. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_15

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