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Comparing Sequence Classification Algorithms for Protein Subcellular Localization

  • Fabrizio Costa
  • Sauro Menchetti
  • Paolo Frasconi
Part of the Studies in Computational Intelligence book series (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.

Keywords

Polypeptide Macromolecule Convolution Sorting Archaea 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fabrizio Costa
    • 1
  • Sauro Menchetti
    • 1
  • Paolo Frasconi
    • 1
  1. 1.Machine Learning and Neural Networks Group Dipartimento di Sistemi e InformaticaUniversità degli Studi di FirenzeItaly

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