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Applications of concurrent sequential patterns in protein data mining

  • Cuiqing Wang
    ,
  • Jing Lu
    ,
  • Malcolm Keech
  • Shenyang Institute of Chemical Technology
    ,
  • Solent University
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

Protein sequences of the same family typically share common patterns which imply their structural function and biological relationship. Traditional sequential patterns mining has its focus on mining frequently occurring sub-sequences. However, a number of applications motivate the search for more structured patterns, such as protein motif mining. This paper builds on the original idea of structural relation patterns and applies the Concurrent Sequential Patterns (ConSP) mining approach in bioinformatics. Specifically, a new method and algorithms are presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Experiments with real-world protein datasets highlight the applicability of the ConSP methodology in protein data mining. The results show the potential for knowledge discovery in the field of protein structure identification.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 01/01/2014

Publication status

Published - 01/01/2014

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

ISBN (Print)

9783319089782

ISBN (Electronic)

9783319089782

External Publication IDs

  • handle.net: 10547/333888
  • Scopus: 84958524840

Host publication title

Machine Learning and Data Mining in Pattern Recognition

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