ECOH: An Enzyme Commission number predictor using mutual information and a support vector machine

Bioinformatics

Volumn 29, Issue 3, 2013, Pages 365-372

  Matsuta, Yoshihiko ^a   Ito, Masahiro ^a   Tohsato, Yukako ^a  

^a RITSUMEIKAN UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ENZYME;

ALGORITHM; ARTICLE; CLASSIFICATION; NOMENCLATURE; SUPPORT VECTOR MACHINE;

ALGORITHMS; ENZYMES; SUPPORT VECTOR MACHINES; TERMINOLOGY AS TOPIC;

MLCS; MLOWN;

EID: 84873311233     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts700     Document Type: Article

References (38)

1. Almonacid, D.E. and Babbitt, P.C. (2011) Toward mechanistic classification of enzyme functions. Curr. Opin. Chem. Biol., 15, 435-442.
2. Andreeva, A. et al. (2008) Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res., 36, D419-D425. (Pubitemid 351149760)
3. Ashburner, M. et al. (2000) Gene ontology: tool for the unification of biology. Nat. Genet., 25, 25-29. (Pubitemid 30257031)
4. Babbitt, P.C. and Gerlt, J.A. (1997) Understanding enzyme superfamilies. Chemistry as the fundamental determinant in the evolution of new catalytic activities. J. Biol. Chem., 272, 30591-30594. (Pubitemid 27527485)
5. Bray, T. et al. (2009) Sequence and structural features of enzymes and their active sites by EC Class. J. Mol. Biol., 386, 1423-1436.
6. Cao, Y. et al. (2008) A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Bioinformatics, 24, i366-i374. (Pubitemid 351911692)
7. Chang, C.C. and Lin, C.J. (2011) LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, 27.
8. Cuff, A.L. et al. (2011) Extending CATH: increasing coverage of the protein structure universe and linking structure with function. Nucleic Acids Res., 39, D420-D426.
9. De Ferrari, L. et al. (2012) EnzML: Multi-label prediction of enzyme classes using InterPro signatures. BMC Bioinformatics, 13, 61.
10. Dobson, P.D. and Doig, A.J. (2005) Predicting enzyme class from protein structure without alignments. J. Mol. Biol., 345, 187-199. (Pubitemid 39572835)
11. Egelhofer, V. et al. (2010) Automatic assignment of EC numbers. PLoS Comput. Biol., 6, e1000661.
12. Furnham, N. et al. (2009) Missing in action: Enzyme functional annotations in biological databases. Nature Chemical Biology, 5, 521-525.
13. Guyon, I. and Elisseeff, A. (2003) An introduction to variable and feature selection. J. Mach. Learn. Res., 3, 1157-1182.
14. He, H.B. and Garcia, E.A. (2009) Learning from imbalanced data. IEEE Trans. Knowl. Data Eng., 21, 1263-1284.
15. Holliday, G.L. et al. (2012) MACiE: Exploring the diversity of biochemical reactions. Nucleic Acids Res., 40, D783-D789.
16. Hsu, C.W. et al. (2003) A practical guide to support vector classification. Bioinformatics, 1, 1-16.
17. Hu, X. and Garcia, E.A. (2010) Similarity perception of reactions catalyzed by oxidoreductases and hydrolases using different classification methods. J. Chem. Inf. Model., 50, 1089-1100.
18. IUPAC-IUBMB. (1999) IUPAC-IUBMB Joint Commission on Biochemical Nomenclature (JCBN) and Nomenclature Committee of IUBMB (NC-IUBMB), Newsletter 1999. Eur. J Biochem., 264, 607-609.
19. Japkowicz, N. and Stephen, S. (2002) The class imbalance problem: a systematic study. Intel. Data Anal., 6, 429-449.
20. Kanehisa, M. et al. (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res., 40, D109-D114.
21. Kotera, M. et al. (2004a) RPAIR: a reactant-pair database representing chemical changes in enzymatic reactions. Genome Informatics, 15, P062.
22. Kotera, M. et al. (2004b) Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions. J. Am. Chem. Soc., 126, 16487-16498. (Pubitemid 40081948)
23. Latino, D.A. and Aires-de-Sousa, J. (2009) Assignment of EC numbers to enzymatic reactions withMOLMAP reaction descriptors and random forests. J. Chem. Inf. Model., 49, 1839-1846.
24. Leber, M. et al. (2009) Automatic assignment of reaction operators to enzymatic reactions. Bioinformatics, 25, 3135-3142.
25. Lu, L. et al. (2007) ECS: an automatic enzyme classifier based on functional domain composition. Comput. Biol. Chem., 31, 226-232. (Pubitemid 46765209)
26. Manning, C.D. et al. (2008) Introduction to Information Retrieval. Cambridge University Press, Cambridge.
27. McGregor, M.J. and Pallai, P.V. (1997) Clustering of large databases of compounds: using MDL "keys" as structural descriptors. J. Chem. Inf. Comput. Sci., 37, 443-448. (Pubitemid 127603548)
28. Nath, N. and Mitchell, J.B. (2012) Is EC class predictable from reaction mechanism? BMC Bioinformatics, 13, 60.
29. Sacher, O. et al. (2009) Investigations of enzyme-catalyzed reactions based on physicochemical descriptors applied to hydrolases. J. Chem. Inf. Model., 49, 1525-1534.
30. Saigo, H. et al. (2010) Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism. BMC Bioinformatics, 11, S31.
31. Steinbeck, C. et al. (2003) The Chemistry Development Kit (CDK): an open-source Java library for chemo-and bioinformatics. J. Chem. Inf. Comput. Sci., 43, 493-500.
32. Tohsato, Y. et al. (2000) A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy. Proc. Int. Conf. Intell. Syst. Mol. Biol., 2000, 376-383.
33. Tonnelier, C. et al. (1991) Machine learning of generic reactions: 3. An efficient algorithm for maximal common substructure determination. Tetrahedron Comput. Methodol., 3, 351-358.
34. Vapnik, V.N. (1998) Statistical Learning Theory. Wiley, New York.
35. Wang, Y. and Liu, X. (2011) Prediction of silicon content in hot metal based on SVM and mutual information for feature selection. J. Inf. Comput. Sci., 8, 4275-4283.
36. Yamanishi, Y. et al. (2009) E-zyme: Predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinformatics, 25, i179-186.
37. You, M.Z. et al. (2011) MAPLSC: A novel multi-class classifier for medical diagnosis. Int. J. Data Min. Bioinf., 5, 383-401.
38. Yu, C. et al. (2009) Genome-wide enzyme annotation with precision control: Catalytic families (CatFam) databases. Proteins, 74, 449-460.