A. Amidi, S. Amidi, D. Vlachakis, N. Paragios, and E. Zacharaki, A Machine Learning Methodology for Enzyme Functional Classification Combining Structural and Protein Sequence Descriptors, Lecture Notes in Computer Science, vol.9656, pp.728-738, 2016.
DOI : 10.1007/978-3-319-31744-1_63

URL : https://hal.archives-ouvertes.fr/hal-01359157

A. Atiya, -Nearest Neighbor Rule, Neural Computation, vol.12, issue.3, pp.731-740, 2005.
DOI : 10.1109/18.312167

K. Borgwardt, C. Ong, S. Schönauer, S. Vishwanathan, A. Smola et al., Protein function prediction via graph kernels, Bioinformatics, vol.21, issue.Suppl 1, pp.47-56, 2005.
DOI : 10.1093/bioinformatics/bti1007

URL : https://academic.oup.com/bioinformatics/article-pdf/21/suppl_1/i47/524364/bti1007.pdf

R. Concu, M. Dea-ayuela, L. Perez-montoto, F. Bolas-fernandez, F. Prado-prado et al., Proteins, Journal of Proteome Research, vol.8, issue.9, pp.4372-4382, 1021.
DOI : 10.1021/pr9003163

R. Concu, M. Dea-ayuela, L. Perez-montoto, F. Uriarte, F. Bolas-fernandez et al., 3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites, BBA)?Proteins and Proteomics 1794, pp.1784-1794, 2009.
DOI : 10.1016/j.bbapap.2009.08.020

R. Concu, G. Podda, E. Uriarte, and H. Gonzalez-diaz, Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials, Journal of Computational Chemistry, vol.67, issue.Database issue, pp.1510-1520, 2009.
DOI : 10.1002/jcc.21170

K. Dave and H. Panchal, ENZPRED-Enzymatic Protein Class Predicting by Machine Learning, Current Topics in Medicinal Chemistry, vol.13, issue.14, pp.1674-1680, 2013.
DOI : 10.2174/15680266113139990118

M. Des-jardins, P. Karp, M. Krummenacker, T. Lee, and C. Ouzounis, Prediction of enzyme classification from protein sequence without the use of sequence similarity, Proceedings of the International Conference on Intelligent Systems for Molecular Biology, pp.92-99, 1997.

D. Devos and A. Valencia, Practical limits of function prediction, 1<98::AID-PROT120>3.0.CO;2-S, pp.98-107, 2000.
DOI : 10.1002/1097-0134(20001001)41:1<98::AID-PROT120>3.0.CO;2-S

P. Dobson and A. Doig, Predicting Enzyme Class From Protein Structure Without Alignments, Journal of Molecular Biology, vol.345, issue.1, pp.187-199, 2005.
DOI : 10.1016/j.jmb.2004.10.024

URL : http://cbio.ensmp.fr/~jvert/svn/bibli/local/Dobson2005Predicting.pdf

L. Ferrari, S. Aitken, J. Van-hemert, and I. Goryanin, EnzML: multi-label prediction of enzyme classes using InterPro signatures, BMC Bioinformatics, vol.13, issue.1, pp.61-71, 2012.
DOI : 10.1093/bib/bbp047

I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, Feature Extraction, Foundations and Applications, 2006.

C. Kumar and A. Choudhary, A top-down approach to classify enzyme functional classes and sub-classes using random forest, EURASIP Journal on Bioinformatics and Systems Biology, vol.14, issue.Suppl 9, pp.1-10, 2012.
DOI : 10.1093/protein/14.9.615

B. Lee, H. Lee, J. Lee, and K. Ryu, Classification of Enzyme Function from Protein Sequence based on Feature Representation, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering, pp.741-747, 2007.
DOI : 10.1109/BIBE.2007.4375643

J. Lie and P. Koehl, 3D representations of amino acids???applications to protein sequence comparison and classification, Computational and Structural Biotechnology Journal, vol.11, issue.18, pp.47-58, 2014.
DOI : 10.1016/j.csbj.2014.09.001

G. Madjarov, D. Kocev, D. Gjorgjevikj, and S. Dzeroski, An extensive experimental comparison of methods for multi-label learning, Pattern Recognition, vol.45, issue.9, pp.3084-3104, 2012.
DOI : 10.1016/j.patcog.2012.03.004

A. Mohammed and C. Guda, Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism, BMC Genomics, vol.16, issue.Suppl 7, pp.16-26, 2015.
DOI : 10.1093/nar/gkl124

C. Munteanu, H. Gonzalez-diaz, and A. Magalhaes, Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices, Journal of Theoretical Biology, vol.254, issue.2, pp.476-482, 2008.
DOI : 10.1016/j.jtbi.2008.06.003

S. Needleman and C. Wunsch, A general method applicable to the search for similarities in the amino acid sequence of two proteins, Journal of Molecular Biology, vol.4870, issue.3, pp.443-453, 1970.

M. Osman, C. Liong, and I. , Hybrid learning algorithm in neural network system for enzyme classification, International Journal of Advances in Soft Computing and its Applications, vol.2, issue.2, pp.209-220, 2010.

J. Platt, Probabilistic outputs for support vector machines and comparison to regularized likelihood methods Advances in Large Margin Classifiers, pp.61-74, 1999.

H. Shen and K. Chou, EzyPred: A top???down approach for predicting enzyme functional classes and subclasses, Biochemical and Biophysical Research Communications, vol.364, issue.1, pp.53-59, 2007.
DOI : 10.1016/j.bbrc.2007.09.098

T. Smith and M. Waterman, Identification of common molecular subsequences, Journal of Molecular Biology, vol.147, issue.1, pp.195-197, 1981.
DOI : 10.1016/0022-2836(81)90087-5

URL : http://www.cmb.usc.edu/papers/msw_papers/msw-042.pdf

A. Todd, C. Orengo, and J. Thornton, Evolution of function in protein superfamilies, from a structural perspective 1 1Edited by A. R. Fersht, Journal of Molecular Biology, vol.307, issue.4, pp.1113-1143, 2001.
DOI : 10.1006/jmbi.2001.4513

G. Tsoumakas and I. Katakis, Multi-Label Classification, International Journal of Data Warehousing and Mining, vol.3, issue.3, pp.1-13, 2007.
DOI : 10.4018/jdwm.2007070101

A. Valencia, Automatic annotation of protein function, Current Opinion in Structural Biology, vol.15, issue.3, pp.267-274, 2005.
DOI : 10.1016/j.sbi.2005.05.010

A. Volkamer, D. Kuhn, F. Rippmann, and M. Rarey, Predicting enzymatic function from global binding site descriptors, Proteins: Structure, Function, and Bioinformatics, vol.95, issue.Suppl 6, pp.479-489, 2013.
DOI : 10.1073/pnas.95.26.15189

V. Volpato, A. Adelfio, and G. Pollastri, Accurate prediction of protein enzymatic class by N-to-1 Neural Networks, BMC Bioinformatics, vol.14, issue.Suppl 1, pp.11-21, 2013.
DOI : 10.1186/1472-6807-9-5

Y. Wang, R. Jing, Y. Hua, Y. Fu, X. Dai et al., Classification of multi-family enzymes by multi-label machine learning and sequence-based descriptors, Analytical Methods, vol.307, issue.17, pp.6832-6840, 1039.
DOI : 10.1006/jmbi.2001.4513

S. Yadav and A. Tiwari, Classification of Enzymes Using Machine Learning Based Approaches: A Review, Machine Learning and Applications: An International Journal, vol.2, issue.3/4, pp.30-49, 2015.
DOI : 10.5121/mlaij.2015.2404

M. Zhang and Z. Zhou, Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization, IEEE Transactions on Knowledge and Data Engineering, vol.18, issue.10, pp.1338-1351, 2006.
DOI : 10.1109/TKDE.2006.162

H. Zou and X. X. , Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou???s General Pseudo Amino Acid Composition, The Journal of Membrane Biology, vol.10, issue.4, pp.551-557, 2016.
DOI : 10.2174/1570164611310010002

Q. Zou, W. Chen, Y. Huang, X. Liu, and Y. Jiang, Identifying Multi-Functional Enzyme by Hierarchical Multi-Label Classifier, Journal of Computational and Theoretical Nanoscience, vol.10, issue.4, 2013.
DOI : 10.1166/jctn.2013.2804