Protein Sequence Classification Using Feature Hashing Metadata

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Title

  • Main Title Protein Sequence Classification Using Feature Hashing

Creator

  • Author: Caragea, Cornelia
    Creator Type: Personal
    Creator Info: University of North Texas
  • Author: Silvescu, Adrian
    Creator Type: Personal
    Creator Info: Naviance, Inc.
  • Author: Mitra, Prasenjit
    Creator Type: Personal
    Creator Info: Pennsylvania State University

Publisher

  • Name: BioMed Central Ltd.
    Place of Publication: [London, United Kingdom]

Date

  • Creation: 2012-06-21

Language

  • English

Description

  • Physical Description: 8 p.: ill.
  • Content Description: Article on protein sequence classification using feature hashing.

Subject

  • Keyword: feature hashing
  • Keyword: variable length k-grams
  • Keyword: dimensionality reduction

Source

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

Citation

  • Publication Title: Proteome Science
  • Volume: 10
  • Issue: Suppl 1
  • Pages: 8
  • Peer Reviewed: True

Collection

  • Name: UNT Scholarly Works
    Code: UNTSW

Institution

  • Name: UNT College of Engineering
    Code: UNTCOE

Rights

  • Rights Access: public

Resource Type

  • Article

Format

  • Text

Identifier

  • DOI: 10.1186/1477-5956-10-S1-S14
  • Archival Resource Key: ark:/67531/metadc181699

Degree

  • Academic Department: Computer Science and Engineering

Note

  • Display Note: This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Proteome Science.
  • Display Note: 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 a recently introduced feature hashing technique to protein sequence classification, where the original high-dimensional space is "reduced" by hashing the features, using a hash function, into a lower-dimensional space, i.e., mapping features to hash keys, where multiple features can be mapped (at random) to the same hash key, and "aggregating" their counts. We compare feature hashing with the "bag of k-grams" and feature selection approaches. Our results show that feature hashing is an effective approach to reducing dimensionality on protein sequence classification tasks.