Evaluating protein motif significance measures: A case study on prosite patterns

Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Volumn , Issue , 2007, Pages 171-178

  Ferreira, Pedro Gabriel ^a   Azevedo, Paulo J ^a  

^a UNIVERSITY OF MINHO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION METHODS; DATA MINING; PATTERN RECOGNITION;

PROSITE PATTERNS; PROTEIN SEQUENCES;

PROTEINS;

EID: 34548784572     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIDM.2007.368869     Document Type: Conference Paper

References (36)

1. E. Ukkonen, J. Vilo, A. Brazma, and I. Jonassen. Discovering patterns and subfamilies in biosequences. In Proceedings of Int'l Conference on Intelligent Systems for Molecular Biology, ISMB 1996.
2. N. M. Abramson. Information Theory and Coding. McGraw-Hill, New York, 1963.
3. A. Apostolico, M. Comin, and L. Parida. Conservative extraction of over-represented extensible motifs. Bioinformatics, 21(1):i9-i18, 2005.
4. N. Hulo, A. Bairoch, V. Bulliard, L. Cerrutti, E. De Castro, P. Langendijk-Genevaux, et al. The Prosite Database. Nucleic Acids Research, 34(Database Issue):D227-D230, 2006.
5. P. Baldi, S. Brunak, Y. Chauvin, C. Andersen, and H. Nielsen. Assessing the accuracy of prediction algorithms for classification: an overview. Bionformutics, 16(5):412-242, February 2000.
6. A. Ben-Hur and D. Brutlag. Remote homology detections motif based approach. Bioinformatics, 19(1):26-33, 2003.
7. A. Ben-Hur and D. Brutlag. Sequence motifs: highly predictive features of protein function. In Proceedings of Workshop on Feature Selection, Neural Information Processing Systems, December 2003.
8. K. Blekas, D. Fotiadis, and A. Likas. Motif-based protein sequence classification using neural networks. Journal of Computational Biology, 12(1):64-82, February 2005.
9. A. Brazma, I. Jonassen, I. Eidhammer, and D. Gilbert. Approaches to the automatic discovery of patterns in biosequences. Technical Report 113, Universit of Bergen, Dep. of Informatics, Bergen, Norway, December 1995.
10. J. Buhler and M. Tompa. Finding motifs using random projections. In Proceedings of 9th Int'l Conference On Intelligents Systems for Molecular Biology, Montreal, Canada, April, 22-25, 2001.
11. E. Eskin, W. Grundy, and Y. Singer. Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Journal of Computational Biology, 10(2):187-214, 2003.
12. Swiss-Prot Protein knowledgebase Expasy. http://www.expasy.org/sprot/.
13. E. Gasteiger, A. Gattiker, C.Hoogland, I.Ivanyi, R. Appel and A. Bairoch ExPASy: the proteomics serverfor in-depth protein knowledge and analysis. Nucleic Acids Research, 31(13):3784-3788, 2003.
14. Pedro Ferreira and Paulo Azevedo. Protein sequence classification through relevant sequence mining and bayes classifiers. In Proceedings of 12th Portuguese Conference on Artificial Intelligence, Covilhã, December 2005.
15. G. Bejerano and G. Yona Modeling protein families using probabilistic suffix trees. In ACM press, editor, In the proceedings of 3rd International Conference on Research in Computational Molecular Biology, pages 15-24, 1999.
16. S. S. Henikoff and J. G. Henikoff. Protein family databases. Encyclopedia of Life Sciences, 2001.
17. S.T. Jensen, L. Shen, and J.S. Liu. Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes. Bioinformatics, 21(20):3832-9, October 2005.
18. I. Jonassen, J. Collins and D. Higgins. Finding flexible patterns in unaligned protein sequences. Protein Science, 4(8): 1587-1595, 1995.
19. Anders Krogh. Computational Methods in Molecular Biology, volume 1, Chapter 4 - An Introduction to Hidden Markov Models for Biological Sequences, pages 45-63. Elsevier, 1998.
20. A. Krogh, M. Brown, I. Saira Mian, K. Sjolander and D. Haussler Hidden markov models in computational biology: applications to protein modeling. Journal of Molecular Biology, (235):1501-1531, 1994.
21. Stefano Lonardi. Tutorial - pattern discover in biosequences. 10th Int'l Conference on Intelligent Systems for Molecular Biology, (Edmonton, Canada), August 3-7 2002.
22. C. Nevill-Manning, K. Sethi, T. Wu, and D. Brutlag. Enumerating and ranking discrete motifs. In Proceedings of the 5th Int'l Conference on Intelligent Systems for Molecular Biology, pages 202-209. AAAI Press, 1997.
23. P. Pevner and S. Sze. Combinatorial approaches to finding subtle signals in dna sequences. In Proceedings of 8th Int'l Conference On Intelligents Systems for Molecular Biology, California, USA, 19-23 August 2000.
24. Marie-France Sagot. On motifs in biological sequences. citeseer.ist.psu.edu/473028.html.
25. P. Smyth and R.M. Goodman. Rule Induction Using Information Theory. MIT press, 1990.
26. G. Stolovitzky and A. Califano. Statistical significance of patterns in biosequences. Technical report, IBM Computational Biology Center, October 1978.
27. Ian Witten and Eibe Frank. Data mining. Morgan Kaufmann Publishers, 2001.
28. Thomas D. Wu and Douglas L. Brutlag. Identification of protein motifs using conserved amino acid properties and partitioning techniques. In 3rd Int'l Conference on Intelligent Systems for Molecular Biology, pages 402-410, 1995.
29. J. Yang, P. Yu, and W. Wang. Mining surprising periodic patterns. In Proceedings 7th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, August 2001.
30. Jiong Yang and Wei Wang. Cluseq: Efficient, and effective sequence clustering. In Proceedings of the 19th Int'l Conference on Data Engineering, Bangalore, India, March 2003. IEEE Computer Society.
31. J. H. Zar. Biostatistical Analysis 3rd Edition. Prentice Hall, 1999.
32. E. Koonin and M. Galperin. Sequence-Evolution-Function: Computational Approaches in Comparative Genomics. Kluwer Academic Publishers, 2003.
33. Andy Field. Discovering Statistics Using SPSS, 2nd Edition. Sage Publications Ltd, 2005.
34. B.B. Matthews Comparison of predicted and observed secondary structure of t4 lysozyme. Biochimica et Biophysica Acta, 405:442-451, 1975.
35. G. van den Eijkel. Intelligent Data Analysis, in M. Berthold and D. Hand (Eds.), 2nd Edition. Appendix B: Information-Theoretic Tree and Rule Induction, pages 465-463. Springer, 2003.
36. M. Bramer. Using J-pruning to reduce overfitting in classification trees. Knowledge-Based Systems, 15(5-6):301-308, 2002.