Genome scale prediction of protein functional class from sequence using data mining

Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2000, Pages 384-389

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

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Biology and genetics; Concept learning; Data mining

Indexed keywords

DATABASE SYSTEMS; GENES; LEARNING ALGORITHMS; LOGIC PROGRAMMING; MOLECULAR BIOLOGY; PROTEINS;

SEQUENCE HOMOLOGY;

DATA MINING;

EID: 0034592764     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/347090.347172     Document Type: Conference Paper

References (28)

1. Aha, D., Kibler, D., Albert, M., Instance-based learning algorithms. Machine Learning 6, 37-66 (1991).
2. Altschul, S. F. et al., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl. Acid Res., 25, 3389-3402 (1997).
3. Bairoch, A., Apweiler, R., The SWlSSPROT protein sequence data bank and its new supplement TrEMBL in 1999. Nucleic Acids Res. 27, 49-54 (1999).
4. Blattner, F. R. et al., The complete genome sequence of Escherichia coli K-12. Science 277, 1453-1461 (1997).
5. Brenner, S. E., Errors in gene annotation. Trends in Genetics 15, 132-133 (1999).
6. Brown, M. P. S. et al., Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci., USA 97, 262-267 (2000).
7. C. elegans Sequencing Consortium, Genome Sequence of the Nematode C. elegans: A Platform for Investigating Biology Science 282, 2012-2018 (1998).
8. Cole, S. T. et al., Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 393, 537-544 (1998).
9. Dehaspe, L., Toivonen, H., King, R. D., Finding frequent substructures in chemical compounds, in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, Agrawal, R., Stolorez, P., Piatetsky-Shapiro, G., eds. (AAAI Press, Menlo Park, 1998) pp. 30-36.
10. DeRisi, J. L., Iyer, V. R., Brown, P. O., Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680-686 (1997).
11. Duda, R., Hart, P., Pattern Classification and Scene Analysis (John Wiley, New York, 1973).
12. Goffeau, A. et al., Life with 6000 genes. Science 274, 546-567 (1996).
13. Henikoff, S., et al. Gene families: the taxonomy of protein paralogs and chimeras. Science 278 609-614 (1997)
14. http://www.sanger.ac.uk/Projects/M_tuberculosis/gene_list_full.shtm (15/03/1999).
15. http://www.who.int/inf-fs/en/fact104.html
16. Kell, D., King, R. D., On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends in Biotechnology 18, 93-98 (2000).
17. Marcotte, E. M., Pellegrine, M., Thompson, M. J., Yeates, T. O., Eisenberg, D., A combined algorithm for genome wide prediction of protein function. Nature 402, 83-86 (1999).
18. Mitchell, T. M., Machine Learning (McGraw-Hill, New York, 1997).
19. Muggleton, S., Inductive logic programming New Generation Computing 8, 295-318 (1991).
20. Munakata, T., Knowledge Discovery. Comm. ACM 41 26-29 (1999)
21. Murzin, A. G., Brenner, S. E., Hubbard, T., Chothia, C., SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247, 536-540 (1995).
22. Park, J., Teichmann, S. A., Hubbard, T., Chothia, C., Intermediate Sequences Increase the Detection of Homology Between Sequences. J. Mol. Biol. 273, 349-354 (1997).
23. Pearson, W. R., Lipman, D. J., Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. USA, 85, 2444-2448 (1988).
24. Piatetsky-Shapiro, G., Frawley W., Knowledge Discovery in Databases (MIT Press, 1991).
25. Provost, F., Fawcett, T., Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In Proceedings of KDD-97 Heckerman, D., Mannila, H., Pregibon, D., eds. (AAAI Press, Menlo Park, 1997) pp 43-48.
26. Quinlan, R., C4.5: Programs for machine learning (Morgan Kaufmann, San Mateo, 1993).
27. Tatusov, R. L., Koonin, E. V., Lipman, D. J., A Genomic Perspective on Protein Families Science 278, 631-637 (1997).
28. Taylor, W. R., Dynamic Sequence Databank Searching with Templates and Multiple Alignments. J. Mol. Biol., 280, 375-406 (1998).