Protein Sequence Classification Using Probabilistic Motifs and Neural Networks-Reference-Cited by-同舟云学术

Protein Sequence Classification Using Probabilistic Motifs and Neural Networks

Published:2003 Issue: Volume: Page:702-709
ISSN:0302-9743
Container-title:Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003
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Author:

Blekas Konstantinos,Fotiadis Dimitrios I.,Likas Aristidis

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/3-540-44989-2_84

Reference9 articles.

1. Hughey R. and Krogh A. Hidden Markov models for sequence analysis: Extension and analysis of the basic method. CABIOS, 12(2):95–107, 1996.

2. Wang J.T.L., Ma Q., Shasha D., and Wu C.H. New techniques for extracting features from protein sequences. IBM: Systems Journal, 40(2):426–441, 2001.

3. Bréjova B., DiMarco C., Vinař T., Hidalgo S.R., Holguin G., and Patten C. Finding patterns in biological sequences. Project Report for CS798g, University of Waterloo, 2000.

4. Ma Q. and Wang J.T.L. Application of Bayesian neural networks to protein sequence classification. In ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 305–309, Boston, MA, USA, Aug 2000.

5. Bailey T.L. and Gribskov M. Combining evidence using p-values: application to sequence homology searches. Bioinformatics, 14:48–54, 1998.

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