The utility of different representations of protein sequence for predicting functional class

Bioinformatics

Volumn 17, Issue 5, 2001, Pages 445-454

  King, Ross D ^a   Karwath, Andreas ^a   Clare, Amanda ^a   Dehaspe, Luc ^b  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

^b PharmaDM   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; BACTERIAL GENE; CLASSIFICATION; CONTROLLED STUDY; ESCHERICHIA COLI; INTERMETHOD COMPARISON; MODEL; NONHUMAN; OPEN READING FRAME; PHYLOGENY; PREDICTION; PRIORITY JOURNAL; PROTEIN STRUCTURE; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY;

BACTERIA (MICROORGANISMS); ESCHERICHIA COLI; NEGIBACTERIA;

EID: 0035006162     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/17.5.445     Document Type: Article

References (37)

1. Aha, D., Kibler, D. and Albert, M. (1991) Instance-based learning algorithms. Mach. Learn., 6, 37-66.
2. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 25, 3389-3402.
3. Bairoch, R. and Apweiler, A. (1999) The SWISSPROT protein sequence data bank and its supplement TrEMBL in 1999. Nucleic Acids Res., 27, 49.
4. Bauer, E. and Kohavi, R. (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach. Learn., 36, 105-139.
5. Blattner, F.R., Plunkett, G., Bloch, C.A., Perna, N.T., Burland, V., Riley, M., Collado-Vides, J., Glasner, J.D., Rode, C.K., Mayhew, G.F., Gregor, J., Davis, N.W., Kirkpatrick, H.A., Goeden, M.A., Rose, D.J., Mau, B. and Shao, Y. (1997) The complete genome sequence of Escherichia coli K-12. Science, 277, 1453-1461.
6. Brenner, E. (1999) Errors in gene annotation. Trends Genet., 15, 132-133.
7. Dehaspe, L., Toivonen, H. and King, R.D. (1998) Finding frequent substructures in chemical compounds. In Agrawl, R., Stolorez, P. and Piatetsky-Shapiro, G. (eds), Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, CA, pp. 30-36.
8. Dietterich, T.G. (1997) Machine learning research: four current directions. AI Magazine, 18, 97-136.
9. Duda, R. and Hart, P. (1973) Pattern Classification and Scene Analysis. Wiley, New York.
10. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. and Uthurusamy, R. (1996) Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Boston, MA.
11. Henikoff, S., Greene, E.A., Pietrokovski, S., Bork, P., Attwood, T.K. and Hood, L. (1997) Gene families: the taxonomy of protein paralogs and chimeras. Science, 278, 609-614.
12. Jaakkola, T., Diekhans, M. and Haussler, D. (1999) Using the Fischer kernal method to detect remote sequence homologies. Seventh International Conference on Intelligent Systems for Molecular Biology. AAAI Press, New York, CA, pp. 149-155.
13. des Jardins, M., Karp, P.D., Krummenacker, M., Lee, T.J. and Ouzounis, C.A. (1997) 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, 93-99.
14. Jones, D.T., Taylor, W.R. and Thornton, J.M. (1992) A new approach to protein fold recognition. Nature, 358, 86-89.
15. Kelly, L.A., MacCallum, R.M. and Sternberg, M.J.E. (2000) Enhanced genome annotation using structural profiles in the program 3D-PSSM. J. Mol. Biol., 299, 501-522.
16. Kell, D.B. and King, R.D. (2000) On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends Biotechnol., 18, 93-98.
17. King, R.D., Karwath, A., Clare, A. and Dehapse, L. (2000a) Genome scale prediction of protein functional class from sequence using data mining. In Ramakrishnan, R., Stolfo, S. and Bayardo, R. (eds), The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. The Association for Computing Machinery, New York, pp. 384-389.
18. King, R.D., Karwath, A., Clare, A. and Dehapse, L. (2000b) Accurate prediction of protein functional class in the M.tuberculosis and E.coli genomes using data mining. Yeast (Comparative and Functional Genomics), 17, 283-293.
19. Lavrac, N. and Dzeroski, S. (1994) Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, UK.
20. Mallat, S.G. (1989) A theory for multiresolution signal decomposition and wavelet representation. IEEE Trans. Patt. Anal. Mach. Intel., 11, 674-693.
21. Marcotte, M., Pellegrine, M., Thompson, M.J., Yeates, T.O. and Eisenberg, D. (1999) A combined algorithm for genome wide prediction of protein function. Nature, 402, 83-86.
22. Mitchell, T.M. (1997) Machine Learning. McGraw-Hill, New York.
23. Muggleton, S. (1991) Inductive logic programming. New Gener. Comput., 8, 295-318.
24. Munakata, T. (1999) Knowledge discovery. Comm. ACM, 41, 26-29.
25. Ouali, M. and King, R.D. (2000) Cascaded multiple classifiers for secondary structure prediction. Protein Sci., 9, 1162-1176.
26. Pearson, W.R. and Lipman, D.J. (1988) Improved tools for biological sequence comparison. Proc. Natl Acad. Sci. USA, 85, 2444-2448.
27. Piatetsky-Shapiro, G. and Frawley, W. (1991) Knowledge Discovery in Databases. MIT Press, Boston, MA.
28. Provost, F. and Fawcett, T. (1997) Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. In Heckerman, D., Mannila, H. and Pregibon, D. (eds), Proceedings of KDD-97. AAAI Press, Menlo Park, CA, pp. 43-48.
29. Quinlan, R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
30. Riley, M. and Labedan, B. (1996) E.coli gene products: physiological functions and common ancestries. In Neidhardt, F. et al. (eds), Escherichia coli and Salmonella: Cellular and Molecular Biology, pp. 2118-2200.
31. Rison, S.C.G., Hodgman, T.C. and Thornton, J.C. (2000) Comparison of functional annotation schemes for genomes. Funct. Integr. Genomics, 1, 56-69.
32. Stawiski, E.W., Baucom, A.E., Lohr, S.C. and Gregoret, L.M. (2000) Predicting protein function from structure: unique structural features of proteases. Proc. Natl Acad. Sci. USA, 97, 3954-3958.
33. Tatusov, R.L., Koonin, E.V. and Lipman, D.J.A. (1997) Genomic perspective on protein families. Science, 278, 631-637.
34. TB_gene_list, http://www.sanger.ac.uk/Projects/M_tuberculosis/ Gene_List/.
35. The Gene Ontology Consortium, (2000) Gene ontology: tool for the unification of biology. Nature Genet., 25, 25-29.
36. Ullman, J.D. (1988) Principles of Databases and Knowledge-base Systems. Vol. 1, Computer Science Press, Rockville, MD.
37. Wootton, J.C. and Federhen, S. (1993) Statistics of local complexity in amino acid sequences and sequence databases. Comput. Chem., 17, 149-163.