Nuclear localization signal prediction based on sequential pattern mining

Jhih Rong Lin, Jianjun Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nuclear Localization Signals (NLS) are the most direct evidence for nuclear localization of proteins. Despite a couple of NLS prediction methods have been developed, the prediction performance is far from being satisfactory. In this study we proposed a sequential pattern mining based algorithm for identifying NLSs from protein sequences. The experiment results showed that our method can achieve better or comparable prediction performance than existing NLS prediction methods, which indicates that the motif residues discovered by our algorithm are effective features for predicting NLS.

Original languageEnglish (US)
Title of host publication2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
Pages536-538
Number of pages3
DOIs
Publication statusPublished - Nov 26 2012
Externally publishedYes
Event2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012 - Orlando, FL, United States
Duration: Oct 7 2012Oct 10 2012

Publication series

Name2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

Conference

Conference2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
CountryUnited States
CityOrlando, FL
Period10/7/1210/10/12

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Keywords

  • NLS
  • Nuclear localization
  • Sequential pattern mining
  • Sorting signals

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Information Management

Cite this

Lin, J. R., & Hu, J. (2012). Nuclear localization signal prediction based on sequential pattern mining. In 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012 (pp. 536-538). (2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012). https://doi.org/10.1145/2382936.2383013