@inproceedings{459e6657f7924817b6dea6d259a8d54c,
title = "Nuclear localization signal prediction based on sequential pattern mining",
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.",
keywords = "NLS, Nuclear localization, Sequential pattern mining, Sorting signals",
author = "Lin, {Jhih Rong} and Jianjun Hu",
year = "2012",
doi = "10.1145/2382936.2383013",
language = "English (US)",
isbn = "9781450316705",
series = "2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012",
pages = "536--538",
booktitle = "2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012",
note = "2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012 ; Conference date: 07-10-2012 Through 10-10-2012",
}