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Perspectives of Neural-Symbolic Integration pp 23–48Cite as

Comparing Sequence Classification Algorithms for Protein Subcellular Localization

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 77))

We discuss and experimentally compare several alternative classification algorithms for biological sequences. The methods presented in this chapter are all essentially based on different forms of statistical learning, ranging from support vector machines with string kernels, to nearest neighbour using biologically motivated distances. We report about an extensive comparison of empirical results for the problem of protein subcellular localization.

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Authors and Affiliations

  1. Machine Learning and Neural Networks Group Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Via Santa Marta 3, 50139, Firenze, Italy

    Fabrizio Costa, Sauro Menchetti & Paolo Frasconi

Authors
  1. Fabrizio Costa
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  2. Sauro Menchetti
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  3. Paolo Frasconi
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Editor information

Editors and Affiliations

  1. University Clausthal, Julius Albert Strabe 4, 38678, Clausthal-Zellerfeld, Germany

    Barbara Hammer

  2. University of Karlsruhe, 76128, Karlsruhe, Germany

    Pascal Hitzler

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Costa, F., Menchetti, S., Frasconi, P. (2007). Comparing Sequence Classification Algorithms for Protein Subcellular Localization. In: Hammer, B., Hitzler, P. (eds) Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73954-8_2

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  • DOI: https://doi.org/10.1007/978-3-540-73954-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73953-1

  • Online ISBN: 978-3-540-73954-8

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