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, Bioinformatics and Biomedical Engineering, pp.728-738, 2016.
DOI : 10.1007/978-3-319-31744-1_63
URL : https://hal.archives-ouvertes.fr/hal-01359157

S. Amidi, A. Amidi, D. Vlachakis, N. Paragios, and E. Zacharaki, Table 6: Testing performance of dataset II., PeerJ, vol.10, issue.4, pp.3095-3105, 2017.
DOI : 10.7717/peerj.3095/table-6

G. Bermejo, G. Clore, and C. Schwieters, Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures, Protein Science, vol.138, issue.12, pp.1824-1836, 2012.
DOI : 10.1006/jmre.1999.1754

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, 1007.
DOI : 10.1093/bioinformatics/bti1007
URL : https://academic.oup.com/bioinformatics/article-pdf/21/suppl_1/i47/524364/bti1007.pdf

L. Borro, S. Oliveira, M. Yamagishi, A. Mancini, J. Jardine et al., Predicting enzyme class from protein structure using Bayesian classification, Genetics and Molecular Research, vol.5, issue.1, pp.193-202, 2006.

S. Bull, M. Muldoon, and A. Doig, Maximising the Size of Non-Redundant Protein Datasets Using Graph Theory, PLoS ONE, vol.17, issue.2, 2013.
DOI : 10.1371/journal.pone.0055484.s004

C. Cai, L. Han, Z. Ji, X. Chen, and Y. Chen, SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence, Nucleic Acids Research, vol.31, issue.13, pp.3692-3697, 2003.
DOI : 10.1093/nar/gkg600

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 1145.
DOI : 10.1145/1961189.1961199

C. Chen, Y. Tian, X. Zou, P. Cai, and J. Mo, Using pseudo-amino acid composition and support vector machine to predict protein structural class, Journal of Theoretical Biology, vol.243, issue.3, pp.444-448, 2006.
DOI : 10.1016/j.jtbi.2006.06.025

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

A. Godzik, Metagenomics and the protein universe, Current Opinion in Structural Biology, vol.21, issue.3, pp.398-403, 2011.
DOI : 10.1016/j.sbi.2011.03.010

L. Han, C. Cai, J. Z. Cao, Z. Cui, J. Chen et al., Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach, Nucleic Acids Research, vol.32, issue.21, pp.6437-6444, 2004.
DOI : 10.1093/nar/gkh984

W. Huang, H. Chen, S. Hwang, and S. Ho, Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method, Biosystems, vol.90, issue.2, pp.405-413, 2007.
DOI : 10.1016/j.biosystems.2006.10.004

K. Illergård, D. Ardell, and A. Elofsson, Structure is three to ten times more conserved than sequence-A study of structural response in protein cores, Proteins: Structure, Function, and Bioinformatics, vol.360, issue.1, Part 1, pp.499-508, 2009.
DOI : 10.1093/oxfordjournals.molbev.a026310

A. Krizhevsky, I. Sutskever, and G. Hinton, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, pp.1097-1105, 2012.
DOI : 10.1162/neco.2009.10-08-881
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

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.1-14, 2012.
DOI : 10.1093/protein/14.9.615

B. Lee, M. Shin, Y. Oh, H. Oh, and K. Ryu, Identification of protein functions using a machine-learning approach based on sequence-derived properties, Proteome Science, vol.7, issue.1, pp.1-10, 2009.
DOI : 10.1186/1477-5956-7-27

Y. Li and T. Shibuya, Malphite: a convolutional neural network and ensemble learning based protein secondary structure predictor, IEEE International conference on bioinformatics and biomedicine (BIBM). Piscataway: IEEE, pp.1260-1266, 2015.

Z. Lin, J. Lanchantin, and Y. Qi, MUST-CNN: a multilayer shift-and-stitch deep convolutional architecture for sequence-based protein structure prediction, 30th AAAI conference on artificial intelligence. Menlo Park: AAAI, 2016.

L. Lu, Z. Qian, Y. Cai, and Y. Li, ECS: An automatic enzyme classifier based on functional domain composition, Computational Biology and Chemistry, vol.31, issue.3, pp.226-232, 2007.
DOI : 10.1016/j.compbiolchem.2007.03.008

C. Nagao, N. Nagano, and K. Mizuguchi, Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests, PLoS ONE, vol.11, issue.1, 2014.
DOI : 10.1371/journal.pone.0084623.s015

E. Nasibov and C. Kandemir-cavas, Efficiency analysis of KNN and minimum distance-based classifiers in enzyme family prediction, Computational Biology and Chemistry, vol.33, issue.6, pp.461-464, 2009.
DOI : 10.1016/j.compbiolchem.2009.09.002

J. Qiu, J. Huang, S. Shi, and R. Liang, Using the Concept of Chous Pseudo Amino Acid Composition to Predict Enzyme Family Classes: An Approach with Support Vector Machine Based on Discrete Wavelet Transform, Protein & Peptide Letters, vol.17, issue.6, pp.715-722, 2010.
DOI : 10.2174/092986610791190372

M. Sharma and P. Garg, Computational Approaches for Enzyme Functional Class Prediction: A Review, Current Proteomics, vol.11, issue.1, pp.17-22, 2014.
DOI : 10.2174/1570164611666140415225013

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, 2007.
DOI : 10.1016/j.bbrc.2007.09.098

M. Spencer, J. Eickholt, and J. Cheng, A Deep Learning Network Approach to <italic>ab initio</italic> Protein Secondary Structure Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.12, issue.1, pp.103-112, 2014.
DOI : 10.1109/TCBB.2014.2343960

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

A. Vedaldi and K. Lenc, MatConvNet, Proceedings of the 23rd ACM international conference on Multimedia, MM '15, pp.689-692, 2015.
DOI : 10.5244/C.28.6

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.1-10, 2013.
DOI : 10.1186/1472-6807-9-5

G. Wang and R. Dunbrack, PISCES: a protein sequence culling server, Bioinformatics, vol.19, issue.12, pp.1589-1591, 2003.
DOI : 10.1093/bioinformatics/btg224
URL : https://academic.oup.com/bioinformatics/article-pdf/19/12/1589/718266/btg224.pdf

Y. Wang, X. Wang, Z. Yang, and N. Deng, Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature, Protein & Peptide Letters, vol.17, issue.11, pp.1441-1449, 2010.
DOI : 10.2174/0929866511009011441

Y. Wang, Y. Wang, Z. Yang, and N. Deng, Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context, BMC Systems Biology, vol.5, issue.Suppl 1, pp.1-10, 2011.
DOI : 10.1016/j.jtbi.2007.01.016