Prediction of Enzyme Classification from Protein Sequence without the use of Sequence Similarity

Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology, ISMB 1997

Volumn , Issue , 1997, Pages 92-99

  desJardins, Marie ^a   Karp, Peter D ^a   Krummenacker, Markus ^a   Lee, Thomas J ^a   Ouzounis, Christos A ^b  

^a SRI INTERNATIONAL   (United States)

^b WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; CLASSIFICATION (OF INFORMATION); FORECASTING; LEARNING SYSTEMS;

AMINO ACID SEQUENCE; AMINO ACIDS COMPOSITIONS; ENZYME CLASSIFICATION; ENZYME COMMISSIONS; ENZYME FUNCTIONS; GIVEN FEATURES; MACHINE LEARNING TECHNIQUES; PRIMARY SEQUENCES; PROTEIN SEQUENCES; SEQUENCE SIMILARITY;

ENZYMES;

ENZYME; PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; CLASSIFICATION; COMPARATIVE STUDY; FACTUAL DATABASE; GENETICS; SEQUENCE ALIGNMENT;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; ENZYMES; PROTEINS; SEQUENCE ALIGNMENT;

EID: 0030628465     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Aha DW, Kibler D, A. M. 1991. Instance-based learning algorithms. Machine Learning 6:37-66.
2. Altschul, S.; Gish, W.; Miller, W.; Myers, E.; and Lipman, D. 1990. Basic local alignment search tool. J Mol Bio 215:403-410.
3. Bairoch, A., and Boeckmann, B. 1994. The SWISS-PROT protein sequence data bank: current status. Nucleic Acids Res 22:3578-3580.
4. Bairoch, A. 1996. The ENZYME databank in 1995. Nucl Acids Res 24:221-222.
5. Casari, G.; Andrade, A.; Bork, P.; Boyle, J.; Daru-var, A.; Ouzounis, C; Schneider, R.; Tamames, J.; Valencia, A.; and Sander, C. 1995. Challenging times for bioinformatics. Nature 376:647-648.
6. CH, W. 1996. Gene classification artificial neural system. Methods in Enzymology 266:71-88.
7. J, D.; R, K.; and M, S. 1995. Supervised and unsupervised discretization of continuous features. In Proa of the Machine Learning Conference.
8. JR, Q. 1993. C4-5: Programs for Machine Learning. Morgan Kaufinann.
9. Karp, P.; Ouzounis, C; and Paley, S. 1996. Hin-Cyc: A knowledge base of the complete genome and metabolic pathways of H. influenzae. In States, D.; Agarwal, P.; Gaasterleind, T.; Hunter, L.; and Smith, R., eds.. Proc of the Fourth International Conference on Intelligent Systems for Molecular Biology, 116-124. Menlo Park, CA: AAAI Press.
10. NC, K.; GJ, O.; H-P, K.; O, W.; and CR, W. 1996. Methanococcus jannaschii genome: revisited. Microbial and Comparative Genomics 1:329-338.
11. P, B.; C, O.; and C, S. 1994. Prom genome sequences to protein function. Curr Opin Struct Biol 4:393-403.
12. R, K., and D, S. 1995. MLC-I-+: Machine learning library in C-I-I-. See WWW URL http://vw.sgi.com/Technology/mlc
13. Webb, E. C. 1992. Enzyme Nomenclature, 1992: Recommendations of the nomenclature committee of the International Union of Biochemistry and Molecular Biology on the nomenclature and classification of enzymes. Academic Press.
14. WR, T. 1986. The classification of amino acid conservation. J Theor Biol 119:205-218.