License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/DagSemProc.10231.7
URN: urn:nbn:de:0030-drops-27419
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Comin, Matteo ; Verzotto, Davide

Remote Homology Detection of Protein Sequences

10231.CominMatteo.ExtAbstract.2741.pdf (1 MB)


The classification of protein sequences using string kernels
provides valuable insights for protein function prediction. Almost
all string kernels are based on patterns that are not independent,
and therefore the associated scores are obtained using a set of
redundant features. In this talk we will discuss how a class of
patterns, called Irredundant, is specifically designed to address
this issue. Loosely speaking the set of Irredundant patterns is the
smallest class of independent patterns that can describe all
patterns in a string. We present a classification method based on
the statistics of these patterns, named Irredundant Class. Results
on benchmark data show that Irredundant Class outperforms most of
the string kernel methods previously proposed, and it achieves
results as good as the current state-of-the-art methods with a fewer
number of patterns. Unfortunately we show that the information
carried by the irredundant patterns can not be easily interpreted,
thus alternative notions are needed.

BibTeX - Entry

  author =	{Comin, Matteo and Verzotto, Davide},
  title =	{{Remote Homology Detection of Protein Sequences}},
  booktitle =	{Structure Discovery in Biology: Motifs, Networks \& Phylogenies},
  pages =	{1--20},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10231},
  editor =	{Alberto Apostolico and Andreas Dress and Laxmi Parida},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-27419},
  doi =		{10.4230/DagSemProc.10231.7},
  annote =	{Keywords: Classification of protein sequences, irredundant patterns}

Keywords: Classification of protein sequences, irredundant patterns
Collection: 10231 - Structure Discovery in Biology: Motifs, Networks & Phylogenies
Issue Date: 2010
Date of publication: 24.08.2010

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