Protein sequence classification using feature hashing

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Article discussing protein sequence classification using feature hashing.

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8 p.: ill.

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Caragea, Cornelia; Silvescu, Adrian & Mitra, Prasenjit Creation Date: Unknown.


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Article discussing protein sequence classification using feature hashing.

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8 p.: ill.


Abstract: Recent advances in next-generation sequencing technologies have resulted in an exponential increase in the rate at which protein sequence data are being acquired. The k-gram feature representation, commonly used for protein sequence classification, usually results in prohibitively high dimensional input spaces, for large values of k. Applying data mining algorithms to these input spaces may be intractable due to the large number of dimensions. Hence, using dimensionality reduction techniques can be crucial for the performance and the complexity of the learning algorithms. In this paper, we study the applicability of feature hashing to protein sequence classification, where the original high-dimensional space is "reduced" by hashing the features into a low-dimensional space, using a hash function, i.e., by mapping features into hash keys, where multiple features can be mapped (at random) to the same hash key, by "aggregating" their counts. We compare feature hashing with the "bag of k-grams" approach. Our results show that feature hashing is an effective approach to reducing dimensionality on protein sequence classification tasks.


  • Proteome Science, 2012, London: BioMed Central Ltd.


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  • Publication Title: Proteome Science
  • Volume: 10
  • Issue: S14
  • Peer Reviewed: Yes


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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • June 21, 2012

Added to The UNT Digital Library

  • Sept. 6, 2013, 3:22 p.m.

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  • March 27, 2014, 12:50 p.m.

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Caragea, Cornelia; Silvescu, Adrian & Mitra, Prasenjit. Protein sequence classification using feature hashing, article, Date Unknown; [London, United Kingdom]. ( accessed January 23, 2019), University of North Texas Libraries, Digital Library,; crediting UNT College of Engineering.