E. B. Abdallah, M. Folschette, O. Roux, and M. Magnin, ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks, Algorithms for Molecular Biology, vol.22, issue.3, p.20, 2017.
DOI : 10.1007/s11047-016-9608-8
URL : https://hal.archives-ouvertes.fr/hal-01654877

A. Abdo, B. Chen, C. Mueller, N. Salim, and P. Willett, Ligand-Based Virtual Screening Using Bayesian Networks, Journal of Chemical Information and Modeling, vol.50, issue.6, pp.501012-1020, 2010.
DOI : 10.1021/ci100090p

A. Abdo, V. Leclère, P. Jacques, N. Salim, and M. Pupin, Prediction of New Bioactive Molecules using a Bayesian Belief Network, Journal of Chemical Information and Modeling, vol.54, issue.1, pp.30-36, 2014.
DOI : 10.1021/ci4004909
URL : https://hal.archives-ouvertes.fr/hal-01090611

W. Abou-jaoudé, P. Traynard, P. T. Monteiro, J. Saez-rodriguez, T. Helikar et al., Logical Modeling and Dynamical Analysis of Cellular Networks, Frontiers in Genetics, vol.105, issue.10, 2016.
DOI : 10.1073/pnas.0806447105

A. Abu-srhan, A. Daoud, and E. , A Hybrid Algorithm Using a Genetic Algorithm and Cuckoo Search Algorithm to Solve the Traveling Salesman Problem and its Application to Multiple Sequence Alignment, International Journal of Advanced Science and Technology, vol.61, pp.29-38, 2013.
DOI : 10.14257/ijast.2013.61.04

V. Acuna, F. Chierichetti, V. Lacroix, A. Marchetti-spaccamela, M. Sagot et al., Modes and cuts in metabolic networks: Complexity and algorithms, Biosystems, issue.1, pp.9551-60, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00428239

B. Adhikari, D. Bhattacharya, R. Cao, and J. Cheng, CONFOLD: Residue-residue contact-guided ab initio protein folding, Proteins: Structure, Function, and Bioinformatics, issue.8, pp.831436-1449, 2015.

T. Akutsu, A bisection algorithm for grammar-based compression of ordered trees, Information Processing Letters, vol.110, issue.18-19, pp.18-19815, 2010.
DOI : 10.1016/j.ipl.2010.07.004

T. Akutsu, M. Hayashida, W. Ching, and M. K. Ng, Control of Boolean networks: Hardness results and algorithms for tree structured networks, Journal of Theoretical Biology, vol.244, issue.4, pp.670-679, 2007.
DOI : 10.1016/j.jtbi.2006.09.023

T. Akutsu, S. Kosub, A. A. Melkman, and T. Tamura, Finding a Periodic Attractor of a Boolean Network, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, issue.5, pp.1410-1421, 2012.
DOI : 10.1109/TCBB.2012.87

R. Albert and J. Thakar, Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, vol.23, issue.Suppl 1, pp.353-369, 2014.
DOI : 10.1063/1.4807733

D. Allouche, I. André, S. Barbe, J. Davies, S. De-givry et al., Computational protein design as an optimization problem, Artificial Intelligence, vol.212, pp.59-79, 2014.
DOI : 10.1016/j.artint.2014.03.005
URL : https://hal.archives-ouvertes.fr/hal-01268554

D. Alocci, J. Mariethoz, O. Horlacher, J. T. Bolleman, M. P. Campbell et al., Property graph vs rdf triple store:A comparison on glycan substructure search, PLOS ONE, issue.12, pp.101-118, 2015.

M. R. Aniba, O. Poch, A. Marchler-bauer, and J. D. Thompson, AlexSys: a knowledge-based expert system for multiple sequence alignment construction and analysis, Nucleic Acids Research, vol.320, issue.5883, p.386338, 2010.
DOI : 10.1126/science.1158395

I. Antonov and M. Borodovsky, GENETACK: FRAMESHIFT IDENTIFICATION IN PROTEIN-CODING SEQUENCES BY THE VITERBI ALGORITHM, Journal of Bioinformatics and Computational Biology, vol.261, issue.03, pp.535-551, 2010.
DOI : 10.1017/CBO9780511790492

K. F. Aoki-kinoshita, Analyzing Glycan-Binding Patterns with the ProfileP- STMM Tool, pp.193-202, 2015.

K. F. Aoki-kinoshita, N. Ueda, H. Mamitsuka, and M. Kanehisa, ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains, Bioinformatics, vol.22, issue.14, pp.22-25, 2006.
DOI : 10.1093/bioinformatics/btl244

G. Arellano, J. Argil, E. Azpeitia, M. Benítez, M. Carrillo et al., "Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis, BMC Bioinformatics, vol.12, issue.1, p.490, 2011.
DOI : 10.1145/876638.876643

S. J. Aronson and H. L. Rehm, Building the foundation for genomics in precision medicine, Nature, vol.46, issue.7573, pp.526336-342, 2015.
DOI : 10.1016/j.jbi.2013.07.001

M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler et al., Gene Ontology: tool for the unification of biology, Nature Genetics, vol.9, issue.1, pp.25-29, 2000.
DOI : 10.1091/mbc.9.12.3273

W. Awada, T. M. Khoshgoftaar, D. Dittman, R. Wald, and A. Napolitano, A review of the stability of feature selection techniques for bioinformatics data, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp.356-363, 2012.
DOI : 10.1109/IRI.2012.6303031

R. Backofen and S. Will, A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models, Constraints, vol.92, issue.1, pp.5-30, 2006.
DOI : 10.1007/s10601-006-6848-8

P. Baldi and S. Brunak, Bioinformatics: the Machine Learning Approach, 2001.

P. J. Ballester and J. B. Mitchell, A machine learning approach to predicting protein???ligand binding affinity with applications to molecular docking, Bioinformatics, vol.4, issue.9, pp.1169-1175, 2010.
DOI : 10.1371/journal.pone.0004783

P. Banerjee, V. B. Siramshetty, M. N. Drwal, and R. Preissner, Computational methods for prediction of in vitro effects of new chemical structures, Journal of Cheminformatics, vol.8, issue.Pt 3, p.51, 2016.
DOI : 10.1186/cc3000

P. Barahona and L. Krippahl, Constraint Programming in Structural Bioinformatics, Constraints, vol.4, issue.4, pp.3-20, 2008.
DOI : 10.1007/s10601-007-9036-6

M. Barberis, R. G. Todd, and L. Van-der-zee, Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models, FEMS Yeast Research, vol.62, issue.1, 2017.
DOI : 10.1016/j.molcel.2016.04.026

J. B. Bard and S. Y. Rhee, Ontologies in biology: design, applications and future challenges, Nature Reviews Genetics, vol.32, issue.59, p.213, 2004.
DOI : 10.1093/nar/gkh069

M. Baron and I. Yanai, New skin for the old RNA-Seq ceremony: the age of single-cell multi-omics, Genome Biology, vol.350, issue.1, p.159, 2017.
DOI : 10.1126/science.aad6770

G. Batt, B. Besson, P. Ciron, H. De-jong, E. Dumas et al., Genetic Network Analyzer: A Tool for the Qualitative Modeling and Simulation of Bacterial Regulatory Networks, pp.439-462, 2012.
DOI : 10.1007/978-1-61779-361-5_22
URL : https://hal.archives-ouvertes.fr/hal-00762122

G. Batt, D. Jong, H. Page, M. And-geiselmann, and J. , Symbolic reachability analysis of genetic regulatory networks using discrete abstractions, Automatica, vol.44, issue.4, pp.982-989, 2008.
DOI : 10.1016/j.automatica.2007.08.004
URL : https://hal.archives-ouvertes.fr/hal-00319485

D. Baú, A. J. Martin, C. Mooney, A. Vullo, I. Walsh et al., , 2006.

, Distill: a suite of web servers for the prediction of one-, two-and threedimensional structural features of proteins, BMC Bioinformatics, vol.7, issue.1, p.402

S. Bechhofer, I. Buchan, D. De-roure, P. Missier, J. Ainsworth et al., Why linked data is not enough for scientists, Future Generation Computer Systems, vol.29, issue.2, pp.599-611, 2013.
DOI : 10.1016/j.future.2011.08.004

R. Bellazzi, Big Data and Biomedical Informatics: A Challenging Opportunity, IMIA Yearbook, vol.9, issue.1, p.8, 2014.
DOI : 10.15265/IY-2014-0024

R. Benigni and C. Bossa, Structure alerts for carcinogenicity, and the Salmonella assay system: A novel insight through the chemical relational databases technology, Mutation Research/Reviews in Mutation Research, vol.659, issue.3, pp.248-261, 2008.
DOI : 10.1016/j.mrrev.2008.05.003

S. Beretta, P. Bonizzoni, G. D. Vedova, Y. Pirola, R. et al., Modeling Alternative Splicing Variants from RNA-Seq Data with Isoform Graphs, Journal of Computational Biology, vol.21, issue.1, pp.16-40, 2014.
DOI : 10.1089/cmb.2013.0112

B. Berger and T. Leighton, ) Model is NP-Complete, Journal of Computational Biology, vol.5, issue.1, pp.27-40, 1998.
DOI : 10.1089/cmb.1998.5.27

H. Bhaskar, D. C. Hoyle, and S. Singh, Machine learning in bioinformatics: A brief survey and recommendations for practitioners, Intelligent Technologies in Medicine and BioinformaticsIntelligent Technologies in Medicine and Bioinformatics, pp.1104-1125, 2006.
DOI : 10.1016/j.compbiomed.2005.09.002

C. Bizer, T. Heath, and T. Berners-lee, Linked Data - The Story So Far, International Journal on Semantic Web and Information Systems, vol.5, issue.3, pp.1-22, 2009.
DOI : 10.4018/jswis.2009081901

J. A. Blake and C. J. Bult, Beyond the data deluge: Data integration and bio-ontologies, Journal of Biomedical Informatics, vol.39, issue.3, pp.314-320, 2006.
DOI : 10.1016/j.jbi.2006.01.003

M. Blaszczyk, D. Gront, S. Kmiecik, K. Ziolkowska, M. Panek et al., Coarse-Grained Protein Models in Structure Prediction, pp.25-53, 2014.

M. Blaszczyk, M. Jamroz, S. Kmiecik, and A. Kolinski, CABS-fold: server for the de novo and consensus-based prediction of protein structure, Nucleic Acids Research, vol.292, issue.W1, pp.41-406, 2013.
DOI : 10.1006/jmbi.1999.3091

C. Bock, M. Farlik, C. Sheffield, and N. , Multi-Omics of Single Cells: Strategies and Applications, Trends in Biotechnology, vol.34, issue.8, 2016.
DOI : 10.1016/j.tibtech.2016.04.004

P. Bonizzoni, S. Ciccolella, D. Vedova, G. Soto, and M. , Beyond Perfect Phylogeny, Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics , ACM-BCB '17, pp.1-10, 2017.
DOI : 10.1126/science.1235122

H. Bouziane, B. Messabih, and A. Chouarfia, Effect of simple ensemble methods on protein secondary structure prediction, Soft Computing, vol.4, issue.3, pp.1663-1678, 2015.
DOI : 10.1016/j.nahs.2010.03.004

F. Boyer and A. Viari, Ab initio reconstruction of metabolic pathways, Bioinformatics, vol.19, issue.Suppl 2, pp.26-34, 2003.
DOI : 10.1093/bioinformatics/btg1055

A. B. Brahim and M. Limam, Ensemble feature selection for high dimensional data: a new method and a comparative study Advances in Data Analysis and Classification, pp.1-16, 2017.

L. Brim, M. Ce?ka, and D. ?afránek, Model Checking of Biological Systems, Formal Methods for Dynamical Systems, pp.63-112, 2013.
DOI : 10.1007/s100090050009

D. R. Brooks, E. Erdem, S. T. Erdo?-gan, J. W. Minett, R. et al., Inferring Phylogenetic Trees Using Answer Set Programming, Journal of Automated Reasoning, vol.138, issue.1, pp.471-511, 2007.
DOI : 10.1075/dia.20.2.04min

J. B. Brown, S. Niijima, and Y. Okuno, Compound???Protein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions, Molecular Informatics, vol.2, issue.11-12, pp.11-12906, 2013.
DOI : 10.1038/nchembio817

N. Cannata, M. Schröder, R. Marangoni, and P. Romano, A Semantic Web for bioinformatics: goals, tools, systems, applications, BMC Bioinformatics, vol.9, issue.Suppl 4, p.1, 2008.
DOI : 10.1186/1471-2105-9-S4-S1

G. Caravagna, A. Graudenzi, D. Ramazzotti, R. Sanz-pamplona, L. De-sano et al., Algorithmic methods to infer the evolutionary trajectories in cancer progression, Proceedings of the National Academy of Sciences, pp.113-4025, 2016.

M. Carrillo, P. A. Góngora, R. , and D. A. , An overview of existing modeling tools making use of model checking in the analysis of biochemical networks, Frontiers in Plant Science, vol.3, 2012.
DOI : 10.3389/fpls.2012.00155

J. S. Chauhan, A. Rao, R. , and G. P. , In silico Platform for Prediction of N-, O- and C-Glycosites in Eukaryotic Protein Sequences, PLoS ONE, vol.27, issue.2, pp.1-10, 2013.
DOI : 10.1371/journal.pone.0067008.s021

V. Chelliah, N. Juty, I. Ajmera, R. Ali, M. Dumousseau et al., BioModels: ten-year anniversary, Nucleic Acids Research, vol.6, issue.D1, pp.43-542, 2015.
DOI : 10.1371/journal.pcbi.1000815
URL : https://academic.oup.com/nar/article-pdf/43/D1/D542/7329252/gku1181.pdf

L. Chen, H. Liu, and C. Friedman, Gene name ambiguity of eukaryotic nomenclatures, Bioinformatics, vol.79, issue.4, pp.248-256, 2005.
DOI : 10.1006/geno.2002.6748
URL : https://academic.oup.com/bioinformatics/article-pdf/21/2/248/459432/bth496.pdf

M. Chen, S. Yu, N. Franz, S. Bowers, and B. Ludäscher, Euler/X:A toolkit for logic-based taxonomy integration, 22nd International Workshop on Functional and (Constraint) Logic Programming, Technische Berichte des Instituts fur Informatik der Christian-Albrechts, 2013.

Q. Chen, Y. P. Chen, and C. Zhang, Detecting inconsistency in biological molecular databases using ontologies, Data Mining and Knowledge Discovery, vol.9, issue.2, pp.275-296, 2007.
DOI : 10.1007/s10618-007-0071-0

J. Cheng and P. Baldi, Improved residue contact prediction using support vector machines and a large feature set, BMC Bioinformatics, vol.8, issue.1, p.113, 2007.
DOI : 10.1186/1471-2105-8-113
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-8-113?site=bmcbioinformatics.biomedcentral.com

C. Clark and S. Divvala, PDFFigures 2.0, Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL '16, pp.143-152, 2016.
DOI : 10.1145/2815833.2815834

I. Coluzza, Computational protein design: a review, Journal of Physics: Condensed Matter, vol.29, issue.14, p.29143001, 2017.
DOI : 10.1088/1361-648X/aa5c76

F. Coste, Learning the Language of Biological Sequences, pp.215-247, 2016.
DOI : 10.1007/978-3-662-48395-4_8
URL : https://hal.archives-ouvertes.fr/hal-01244770

M. Cruz-monteagudo, J. L. Medina-franco, Y. Perez-castillo, O. Nicolotti, M. N. Cordeiro et al., Activity cliffs in drug discovery: Dr jekyll or mr hyde? Drug Discovery Today, pp.191069-1080, 2014.
DOI : 10.1016/j.drudis.2014.02.003

J. A. Cuff and G. J. Barton, Application of multiple sequence alignment profiles to improve protein secondary structure prediction, Proteins: Structure, Function, and Genetics, vol.6, issue.3, pp.502-511, 2000.
DOI : 10.1002/pro.5560060606

D. Chapman, S. Adami, C. , O. Wilke, C. et al., Table 11: Treatment top features., PeerJ, vol.69, issue.S8, p.3139, 2017.
DOI : 10.7717/peerj.3139/table-11

D. C. Dallas, A. Guerrero, E. A. Parker, R. C. Robinson, J. Gan et al., Current peptidomics: Applications, purification, identification, quantification, and functional analysis, PROTEOMICS, vol.51, issue.5-6, pp.5-61026, 2015.
DOI : 10.1016/j.jinsphys.2005.04.019
URL : http://europepmc.org/articles/pmc4371869?pdf=render

A. De-brevern, C. Etchebest, and S. Hazout, Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks, Proteins: Structure, Function, and Genetics, vol.7, issue.3, pp.41271-287, 2000.
DOI : 10.1007/BF02337561
URL : https://hal.archives-ouvertes.fr/inserm-00132821

L. De-raedt and S. Kramer, The Levelwise Version Space Algorithm and Its Application to Molecular Fragment Finding, Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp.853-859, 2001.

C. A. Deagustini, M. V. Martinez, M. A. Falappa, and G. R. Simari, , 2016.

, Datalog+-ontology consolidation, Journal of Artificial Intelligence Research, vol.56, pp.613-656

R. Diaz-uriarte, GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest, BMC Bioinformatics, vol.8, issue.1, p.328, 2007.
DOI : 10.1186/1471-2105-8-328

D. Dimova and J. Bajorath, Advances in Activity Cliff Research, Molecular Informatics, vol.5, issue.5, pp.181-191, 2016.
DOI : 10.1039/C5RA01732G

B. Do?-gan and T. Ölmez, A novel state space representation for the solution of 2D-HP protein folding problem using reinforcement learning methods, Applied Soft Computing, vol.26, pp.213-223, 2015.

A. Drozdetskiy, C. Cole, J. Procter, and G. J. Barton, JPred4: a protein secondary structure prediction server, Nucleic Acids Research, vol.18, issue.Suppl. 1, pp.43-389, 2015.
DOI : 10.1093/bioinformatics/btp033

S. P. Dubey, N. G. Kini, S. Balaji, and M. S. Kumar, Protein structure prediction on 2D square HP lattice with revised fitness function, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.1732-1736, 2017.
DOI : 10.1109/ICACCI.2017.8126094

E. Dubrova and M. Teslenko, A SAT-Based Algorithm for Finding Attractors in Synchronous Boolean Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, issue.5, pp.1393-1399, 2011.
DOI : 10.1109/TCBB.2010.20

S. Dunn, L. Wahl, and G. Gloor, Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction, Bioinformatics, vol.146, issue.3, pp.333-340, 2008.
DOI : 10.1126/science.146.3651.1593

S. R. Eddy, Profile hidden Markov models, Bioinformatics, vol.14, issue.9, pp.755-763, 1998.
DOI : 10.1093/bioinformatics/14.9.755

M. El-kebir, L. Oesper, H. Acheson-field, R. , and B. J. , Reconstruction of clonal trees and tumor composition from multi-sample sequencing data, Bioinformatics, vol.31, issue.12, pp.31-62, 2015.
DOI : 10.1126/science.1256930

E. Erdem, Applications of Answer Set Programming in Phylogenetic Systematics, pp.415-431, 2011.
DOI : 10.1016/j.jal.2008.10.001

E. Faraggi and A. Kloczkowski, Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X, pp.45-53, 2017.
DOI : 10.1093/nar/gki402

E. Faraggi, T. Zhang, Y. Yang, L. Kurgan, and Y. Zhou, SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles, Journal of Computational Chemistry, vol.59, issue.3, pp.259-267, 2012.
DOI : 10.1002/prot.20435

M. Feig, P. Rotkiewicz, A. Kolinski, J. Skolnick, and C. L. Brooks, Accurate reconstruction of all-atom protein representations from side-chain-based low-resolution models, Proteins: Structure, Function, and Genetics, vol.37, issue.1, pp.86-97, 2000.
DOI : 10.1080/07391102.1991.10507882

E. A. Feigenbaum and B. G. Buchanan, DENDRAL and Meta-DENDRAL: roots of knowledge systems and expert system applications, Artificial Intelligence, vol.59, issue.1-2, pp.233-240, 1993.
DOI : 10.1016/0004-3702(93)90191-D

L. G. Ferreira, R. N. Dos-santos, G. Oliva, and A. D. Andricopulo, Molecular Docking and Structure-Based Drug Design Strategies, Molecules, vol.146, issue.7, pp.2013384-13421, 2015.
DOI : 10.1002/ardp.201400400

E. Ford, . St, K. John, W. , and W. C. , Towards Improving Searches for Optimal Phylogenies, Systematic Biology, vol.6, issue.1, pp.56-65, 2015.
DOI : 10.1109/TCBB.2008.100

M. Frank and S. Schloissnig, Bioinformatics and molecular modeling in glycobiology. Cellular and molecular life sciences, pp.2749-2772, 2010.

N. M. Franz, M. Chen, S. Yu, P. Kianmajd, S. Bowers et al., Reasoning over Taxonomic Change: Exploring Alignments for the Perelleschus Use Case, PLOS ONE, vol.20, issue.2, pp.1-34, 2015.
DOI : 10.1371/journal.pone.0118247.s002

C. Galiez, C. N. Magnan, F. Coste, and P. Baldi, VIRALpro: a tool to identify viral capsid and tail sequences, Bioinformatics, vol.4, issue.9, pp.321405-1407, 2016.
DOI : 10.1038/nrmicro1750
URL : https://hal.archives-ouvertes.fr/hal-01242251

M. Y. Galperin, X. M. Su-'arez, and D. J. And-rigden, The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes, Nucleic Acids Research, issue.D1, pp.45-46, 2017.
DOI : 10.1093/nar/gkx021

X. Gan, L. Kapsokalivas, A. A. Albrecht, and K. Steinhöfel, A symmetryfree subspace for ab initio protein folding simulations, Bioinformatics Research and Development, pp.128-139, 2008.

Y. Gao, S. Wang, M. Deng, and J. Xu, Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning, 2017.
DOI : 10.1101/236851

, Jacques Nicolas

C. Gawad, W. Koh, and S. R. Quake, Single-cell genome sequencing: current state of the science, Nature Reviews Genetics, vol.148, issue.3, p.17175, 2016.
DOI : 10.1038/onc.2013.29

E. Gawehn, J. A. Hiss, and G. Schneider, Deep Learning in Drug Discovery, Molecular Informatics, vol.9, issue.5, pp.3-14, 2016.
DOI : 10.1002/minf.201400122

J. Gertheiss and G. Tutz, Supervised feature selection in mass spectrometrybased proteomic profiling by blockwise boosting, Bioinformatics, issue.8, pp.251076-1077, 2009.

G. Ghisalberti, M. Masseroli, and L. Tettamanti, Summary, Journal of Integrative Bioinformatics, vol.7, issue.3, pp.52-64, 2010.
DOI : 10.1515/jib-2010-119

A. W. Ghoorah, M. Devignes, M. Smaïl-tabbone, R. , and D. W. , Protein docking using case-based reasoning, Proteins: Structure, Function, and Bioinformatics, vol.78, issue.15, pp.812150-2158, 2013.
DOI : 10.1002/prot.22830
URL : https://hal.archives-ouvertes.fr/hal-00880341

G. H. Gonnet, C. Korostensky, and S. Benner, Evaluation Measures of Multiple Sequence Alignments, Journal of Computational Biology, vol.7, issue.1-2, pp.261-276, 2000.
DOI : 10.1089/10665270050081513

J. J. Gordon, M. W. Towsey, J. M. Hogan, S. A. Mathews, and P. Timms, Improved prediction of bacterial transcription start sites, Bioinformatics, vol.16, issue.1, pp.142-148, 2005.
DOI : 10.1093/bioinformatics/16.1.16

D. Greene, S. Richardson, and E. Turro, ontologyX: a suite of R packages for working with ontological data, Bioinformatics, vol.7, issue.7, pp.1104-1106, 2017.
DOI : 10.1093/bioinformatics/btu684

L. Grivell, Mining the bibliome: searching for a needle in a haystack? EMBO reports, pp.200-203, 2002.

D. Gront, S. Kmiecik, A. Koli´nskikoli´nski, J. H. Meinke, M. T. Zimmermann et al., High throughput method for protein structure prediction, NIC Workshop 2006: From Computational Biophysics to System Biology, pp.79-82, 2006.

Y. Guan, C. L. Myers, D. C. Hess, Z. Barutcuoglu, A. A. Caudy et al., Predicting gene function in a hierarchical context with an ensemble of classifiers, Genome Biology, vol.9, issue.Suppl 1, pp.9-12, 2008.
DOI : 10.1186/gb-2008-9-s1-s3

Y. Guermeur, C. Geourjon, P. Gallinari, and G. Deléage, Improved performance in protein secondary structure prediction by inhomogeneous score combination, Bioinformatics, vol.15, issue.5, pp.413-421, 1999.
DOI : 10.1093/bioinformatics/15.5.413
URL : https://hal.archives-ouvertes.fr/hal-00313796

Y. Guo, F. Tao, Z. Wu, W. , and Y. , Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations, BMC Systems Biology, vol.115, issue.4, p.93, 2017.
DOI : 10.1063/1.1383051

S. K. Gupta, J. D. Kececioglu, and A. A. Schäffer, Improving the Practical Space and Time Efficiency of the Shortest-Paths Approach to Sum-of-Pairs Multiple Sequence Alignment, Journal of Computational Biology, vol.2, issue.3, pp.459-472, 1995.
DOI : 10.1089/cmb.1995.2.459

D. Gusfield, Persistent phylogeny, Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, BCB '15, pp.443-451, 2015.
DOI : 10.1093/acprof:oso/9780199297306.003.0011

S. Götz, J. M. Garc-'ia-g-'omez, J. Terol, T. D. Williams, S. H. Nagaraj et al., Highthroughput functional annotation and data mining with the blast2go suite, Nucleic Acids Research, issue.10, pp.363420-3435, 2008.

Y. Han, S. Gao, K. Muegge, W. Zhang, and B. Zhou, Advanced Applications of RNA Sequencing and Challenges, Bioinformatics and Biology Insights, vol.9, issue.1, pp.9-10, 2015.
DOI : 10.4137/BBI.S28991

A. E. Hassanien, E. T. Al-shammari, and N. I. Ghali, Computational intelligence techniques in bioinformatics, Computational Biology and Chemistry, vol.47, pp.37-47, 2013.
DOI : 10.1016/j.compbiolchem.2013.04.007

A. Hassanien, M. G. Milanova, T. G. Smolinski, A. , and A. , Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives , and Challenges, pp.3-47, 2008.

J. Hastings, P. De-matos, A. Dekker, M. Ennis, B. Harsha et al., The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013, Nucleic Acids Research, vol.35, issue.D1, pp.41-456, 2013.
DOI : 10.1093/nar/gkl958

M. Hatem and W. Ruml, External Memory Best-First Search for Multiple SequenceAlignment, 27th AAAI conference on Artificial Intelligence, 2013.

B. Hayes-roth, B. G. Buchanan, O. Lichtarge, M. Hewitt, R. B. Altman et al., PROTEAN: Deriving protein structure from constraints, AAAI, pp.904-909, 1986.

R. Heffernan, K. Paliwal, J. Lyons, A. Dehzangi, A. Sharma et al., Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning Scientific reports, p.11476, 2015.

C. Helma, S. Kramer, and D. R. Luc, The Molecular Feature Miner MOLFEA, Proceedings of the Beilstein Workshop 2002: Molecular Informatics: Confronting Complexity, pp.79-93, 2002.

G. Hinselmann, L. Rosenbaum, A. Jahn, N. Fechner, C. Ostermann et al., Large-Scale Learning of Structure???Activity Relationships Using a Linear Support Vector Machine and Problem-Specific Metrics, Journal of Chemical Information and Modeling, vol.51, issue.2, pp.203-213, 2011.
DOI : 10.1021/ci100073w

L. Hirschman, J. C. Park, J. Tsujii, L. Wong, and C. H. Wu, Accomplishments and challenges in literature data mining for biology, Bioinformatics, vol.18, issue.12, pp.181553-1561, 2002.
DOI : 10.1093/bioinformatics/18.12.1553

R. Hoehndorf, L. Slater, P. N. Schofield, G. , and G. V. , Aber-OWL: a framework for ontology-based data access in biology, BMC Bioinformatics, vol.25, issue.1-2, p.26, 2015.
DOI : 10.1007/s00335-013-9481-z

K. Hoff and M. Stanke, Current methods for automated annotation of protein-coding genes, Current Opinion in Insect Science, vol.7, pp.8-14, 2015.
DOI : 10.1016/j.cois.2015.02.008

K. J. Hoff, S. Lange, A. Lomsadze, M. Borodovsky, and M. Stanke, , 2015.

, Braker1: unsupervised rna-seq-based genome annotation with genemark-et and augustus, Bioinformatics, vol.32, issue.5, pp.767-769

A. Holzinger, M. Dehmer, and I. Jurisica, Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions, BMC Bioinformatics, vol.15, issue.Suppl 6, pp.15-16, 2014.
DOI : 10.1093/nar/gkn735

M. Hosoda, Y. Akune, and K. F. Aoki-kinoshita, Development and Application of an Algorithm to Compute Weighted Multiple Glycan Alignments, Bioinformatics, vol.33, issue.9, pp.1317-1323, 2017.
DOI : 10.1093/bioinformatics/btw827

D. W. Huang, B. T. Sherman, and R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nature Protocols, vol.99, issue.1, p.44, 2008.
DOI : 10.6026/97320630002428

L. Hundley, J. Lederberg, and E. Levinthal, Multivator-a biochemical laboratory for martian experiments, 1963.

R. P. Huntley, M. A. Harris, Y. Alam-faruque, J. A. Blake, S. Carbon et al., A method for increasing expressivity of Gene Ontology annotations using a compositional approach, BMC Bioinformatics, vol.15, issue.1, p.155, 2014.
DOI : 10.1186/1471-2105-8-S9-S1

T. Ideker and R. Nussinov, Network approaches and applications in biology, PLOS Computational Biology, vol.169, issue.10, pp.1-3, 2017.
DOI : 10.1016/j.cell.2017.05.038

T. Ikeda and H. Imai, Enhanced A??? algorithms for multiple alignments: optimal alignments for several sequences and k-opt approximate alignments for large cases, Theoretical Computer Science, vol.210, issue.2, pp.341-374, 1999.
DOI : 10.1016/S0304-3975(98)00093-0

A. Inokuchi, T. Washio, and H. Motoda, An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data, Principles of Data Mining and Knowledge Discovery, pp.13-23, 2000.
DOI : 10.1007/3-540-45372-5_2

K. Inoue, Logic programming for Boolean networks, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp.924-930, 2011.

I. Inza, B. Calvo, R. Armañanzas, E. Bengoetxea, P. Larrañaga et al., Machine Learning: An Indispensable Tool in Bioinformatics, pp.25-48, 2010.

I. Do?-gan, R. Kim, S. Chatr-aryamontri, A. Chang, C. S. Oughtred et al., The biocbiogrid corpus: full text articles annotated for curation of protein?protein and genetic interactions, Database, issue.1, pp.2017-147, 2017.

S. Jiang and S. Ramachandran, Assigning biological functions to rice genes by genome annotation, expression analysis and mutagenesis, Biotechnology Letters, vol.1, issue.12, pp.1753-1763, 2010.
DOI : 10.1093/mp/ssn043

D. T. Jones, T. Singh, T. Kosciolek, and S. Tetchner, MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins, Bioinformatics, vol.106, issue.7, pp.31999-1006, 2015.
DOI : 10.1073/pnas.0805923106

K. Jong, E. Marchiori, M. Sebag, . Van-der, and A. Vaart, Feature selection in proteomic pattern data with support vector machines, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing (IEEE Cat. No.04CH37612), pp.41-48, 2004.
DOI : 10.1109/CIBCB.2004.1393930

P. N. Judson, Knowledge-Based Approaches for Predicting the Sites and Products of Metabolism, pp.293-318, 2014.
DOI : 10.1039/C2TX20037F

S. Jupp, J. Malone, J. Bolleman, M. Brandizi, M. Davies et al., The EBI RDF platform: linked open data for the life sciences, Bioinformatics, vol.37, issue.8, pp.301338-1339, 2014.
DOI : 10.1093/bioinformatics/btq099

M. Källberg, H. Wang, S. Wang, J. Peng, Z. Wang et al., , 2012.

, Template-based protein structure modeling using the raptorx web server, Nature protocols, vol.7, issue.8, pp.1511-1522

R. Kaminski, T. Schaub, A. Siegel, and S. Videla, Abstract, Theory and Practice of Logic Programming, pp.4-5675, 2013.
DOI : 10.1089/cmb.2009.0121
URL : https://hal.archives-ouvertes.fr/hal-01186999

M. Kanehisa, KEGG GLYCAN, A Practical Guide to Using Glycomics Databases, pp.177-193, 2017.
DOI : 10.1093/nar/gkv1189

P. D. Karpe, M. Latendresse, C. , and R. , The Pathway Tools Pathway Prediction Algorithm, Standards in Genomic Sciences, vol.25, issue.3, p.424, 2011.
DOI : 10.1093/bioinformatics/btp097

J. Kavanagh, D. Mitchell, E. Ternovska, J. Ma?uch, X. Zhao et al., Constructing Camin-Sokal Phylogenies Via Answer Set Programming, pp.452-466, 2006.
DOI : 10.1007/11916277_31

E. Keedwell and A. Narayanan, Intelligent bioinformatics: The application of artificial intelligence techniques to bioinformatics problems, 2005.
DOI : 10.1002/0470015721

P. Khatri, M. Sirota, and A. J. Butte, Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges, PLoS Computational Biology, vol.23, issue.2, pp.1-10, 2012.
DOI : 10.1371/journal.pcbi.1002375.s006

A. Kislyuk, A. Lomsadze, A. L. Lapidus, and M. Borodovsky, Frameshift detection in prokaryotic genomic sequences, International Journal of Bioinformatics Research and Applications, vol.5, issue.4, pp.458-477, 2009.
DOI : 10.1504/IJBRA.2009.027519

D. B. Kitchen, H. Decornez, J. R. Furr, and J. Bajorath, Docking and scoring in virtual screening for drug discovery: methods and applications, Nature Reviews Drug Discovery, vol.44, issue.11, p.935, 2004.
DOI : 10.1021/jm010016f

S. Klamt, Generalized concept of minimal cut sets in biochemical networks, Biosystems, vol.83, issue.2-3, pp.233-247, 2006.
DOI : 10.1016/j.biosystems.2005.04.009

H. Klarner, A. Bockmayr, and H. Siebert, Computing maximal and minimal trap spaces of Boolean networks, Natural Computing, vol.23, issue.2, pp.535-544, 2015.
DOI : 10.1088/1478-3975/9/5/055001

H. Klarner, A. Streck, and H. Siebert, PyBoolNet: a python package for the generation, analysis and visualization of boolean networks, Bioinformatics, vol.14, issue.5, pp.770-772, 2017.
DOI : 10.1016/S0022-5193(05)80350-9

A. Koli´nskikoli´nski, Protein modeling and structure prediction with a reduced representation, Acta Biochimica Polonica, p.51, 2004.

L. Koponen, E. Oikarinen, T. Janhunen, and L. Säilä, Abstract, Theory and Practice of Logic Programming, pp.4-5604, 2015.
DOI : 10.1016/S0166-218X(00)00202-X

R. E. Korf, W. Zhang, I. Thayer, and H. Hohwald, Frontier search, Journal of the ACM, vol.52, issue.5, pp.715-748, 2005.
DOI : 10.1145/1089023.1089024

, Jacques Nicolas

C. Korostensky and G. Gonnet, Near optimal multiple sequence alignments using a traveling salesman problem approach, 6th International Symposium on String Processing and Information Retrieval. 5th International Workshop on Groupware (Cat. No.PR00268), pp.105-114, 1999.
DOI : 10.1109/SPIRE.1999.796584

M. Koshino, H. Murata, M. Shirayama, and H. Kimura, Applying the various optimal solution search methods to multiple sequence alignments and performance evaluation, Systems and Computers in Japan, vol.15, issue.11, pp.371-381, 2006.
DOI : 10.1002/scj.20519

R. Kowalski, Algorithm = logic + control, Communications of the ACM, vol.22, issue.7, pp.424-436, 1979.
DOI : 10.1145/359131.359136

S. Kumozaki, K. Sato, and Y. Sakakibara, A Machine Learning Based Approach to <italic>de novo</italic> Sequencing of Glycans from Tandem Mass Spectrometry Spectrum, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.12, issue.6, pp.1267-1274, 2015.
DOI : 10.1109/TCBB.2015.2430317

V. Lacroix, M. Sammeth, R. Guigo, and A. Bergeron, Exact Transcriptome Reconstruction from Short Sequence Reads, International Workshop on Algorithms in Bioinformatics, pp.50-63, 2008.
DOI : 10.1007/978-3-540-87361-7_5
URL : https://hal.archives-ouvertes.fr/hal-00428220

J. Lai, J. An, I. Seim, C. Walpole, A. Hoffman et al., Fusion transcript loci share many genomic features with non-fusion loci, BMC Genomics, vol.16, issue.6, p.1021, 2015.
DOI : 10.1261/rna.2019810
URL : https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-015-2235-4

A. Lavecchia, Machine-learning approaches in drug discovery: methods and applications. Drug discovery today, pp.318-331, 2015.
DOI : 10.1016/j.drudis.2014.10.012

T. Le, H. Nguyen, E. Pontelli, S. , and T. C. , ASP at Work: An ASP Implementation of PhyloWS, Technical Communications of the 28th International Conference on Logic Programming (ICLP'12), volume 17 of Leibniz International Proceedings in Informatics (LIPIcs) Schloss Dagstuhl?Leibniz- Zentrum fuer Informatik, pp.359-369, 2012.

L. Novere and N. , Quantitative and logic modelling of molecular and gene networks, Nature Reviews Genetics, vol.52, issue.3, p.146, 2015.
DOI : 10.1093/bioinformatics/btq124

A. Lepailleur, G. Poezevara, and R. Bureau, AUTOMATED DETECTION OF STRUCTURAL ALERTS (CHEMICAL FRAGMENTS) IN (ECO)TOXICOLOGY, Computational and Structural Biotechnology Journal, vol.5, issue.6, p.201302013, 2013.
DOI : 10.5936/csbj.201302013
URL : https://hal.archives-ouvertes.fr/hal-01023826

S. Lertampaiporn, C. Thammarongtham, C. Nukoolkit, B. Kaewkamnerdpong, and M. Ruengjitchatchawalya, Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm, Nucleic Acids Research, vol.5, issue.3, pp.42-93, 2014.
DOI : 10.1093/nar/gki256

F. Li, C. Li, M. Wang, G. I. Webb, Y. Zhang et al., GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome, Bioinformatics, vol.15, issue.9, pp.311411-1419, 2015.
DOI : 10.1016/j.chembiol.2008.07.016

H. Li, J. Hou, B. Adhikari, Q. Lyu, and J. Cheng, Deep learning methods for protein torsion angle prediction, BMC Bioinformatics, vol.5, issue.2, p.417, 2017.
DOI : 10.1109/72.279181
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-017-1834-2

L. Li, Y. Zhang, L. Zou, C. Li, B. Yu et al., An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity, PLoS ONE, vol.73, issue.1, pp.1-12, 2012.
DOI : 10.1371/journal.pone.0031057.t008
URL : https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0031057&type=printable

A. Lihu and ¸. S. Holban, A review of ensemble methods for de novo motif discovery in ChIP-Seq data, Briefings in Bioinformatics, vol.42, issue.5, pp.964-973, 2015.
DOI : 10.1093/nar/gkt1288

C. Y. Lim, H. Wang, S. Woodhouse, N. Piterman, L. Wernisch et al., BTR: training asynchronous Boolean models using single-cell expression data, BMC Bioinformatics, vol.24, issue.Suppl 1, p.355, 2016.
DOI : 10.1093/bioinformatics/btn336
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1235-y

R. K. Lindsay, B. G. Buchanan, E. A. Feigenbaum, and J. Lederberg, DENDRAL: A case study of the first expert system for scientific hypothesis formation, Artificial Intelligence, vol.61, issue.2, pp.61209-261, 1993.
DOI : 10.1016/0004-3702(93)90068-M

H. Liu, L. Liu, and H. Zhang, Ensemble gene selection for cancer classification, Pattern Recognition, vol.43, issue.8, pp.432763-2772, 2010.
DOI : 10.1016/j.patcog.2010.02.008

A. Lomsadze, P. D. Burns, and M. Borodovsky, Integration of mapped rnaseq reads into automatic training of eukaryotic gene finding algorithm, Nucleic Acids Research, issue.15, pp.42-119, 2014.

A. Lomsadze, V. Ter-hovhannisyan, Y. O. Chernoff, and M. Borodovsky, , 2005.

, Gene identification in novel eukaryotic genomes by self-training algorithm, Nucleic Acids Research, vol.33, issue.20, pp.6494-6506

C. N. Magnan and P. Baldi, SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity, Bioinformatics, vol.23, issue.10, pp.302592-2597, 2014.
DOI : 10.1093/bioinformatics/btm098

R. Mahadevan, A. Von-kamp, and S. Klamt, Genome-scale strain designs based on regulatory minimal cut sets, Bioinformatics, vol.31, issue.17, pp.312844-2851, 2015.
DOI : 10.1016/j.ymben.2012.09.005

P. Mahé and J. Vert, Graph kernels based on tree patterns for molecules, Machine Learning, pp.3-35, 2009.
DOI : 10.1017/CBO9780511809682

S. Malikic, A. W. Mcpherson, N. Donmez, and C. S. Sahinalp, Clonality inference in multiple tumor samples using phylogeny, Bioinformatics, vol.346, issue.9, pp.311349-1356, 2015.
DOI : 10.1126/science.1256930

H. Mamitsuka, Glycoinformatics: Data Mining-based Approaches, CHIMIA International Journal for Chemistry, vol.65, issue.1, pp.10-13, 2011.
DOI : 10.2533/chimia.2011.10

M. Mann and R. Backofen, Exact methods for lattice protein models. Bio- Algorithms and Med-Systems, pp.213-225, 2014.

M. Mann, S. Will, and R. Backofen, CPSP-tools ??? Exact and complete algorithms for high-throughput 3D lattice protein studies, BMC Bioinformatics, vol.9, issue.1, p.230, 2008.
DOI : 10.1186/1471-2105-9-230

M. Martin, P. Dague, S. Pérès, and L. Simon, Minimality of Metabolic Flux Modes under Boolean Regulation Constraints, 12th International Workshop on Constraint-Based Methods for Bioinformatics WCB'16, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01483433

N. Matentzoglu, M. Vigo, C. Jay, and R. Stevens, Inference inspector: Improving the verification of ontology authoring actions, Web Semantics: Science, Services and Agents on the World Wide Web, 2017.

J. Maupetit, R. Gautier, and P. Tufféry, SABBAC: online Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace, Nucleic Acids Research, vol.34, issue.Web Server, pp.147-151, 2006.
DOI : 10.1093/nar/gkl289

L. J. Mcguffin, K. Bryson, and D. T. Jones, The PSIPRED protein structure prediction server, Bioinformatics, vol.16, issue.4, pp.404-405, 2000.
DOI : 10.1093/bioinformatics/16.4.404

I. Medina, J. Mart-'inez, H. Barrachina, S. Castillo, M. I. Paschall et al., Highly sensitive and ultrafast read mapping for RNA-seq analysis, DNA Research, vol.23, issue.2, pp.93-100, 2016.
DOI : 10.1038/msb.2012.61

S. Mei, Multi-Label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization, PLoS ONE, vol.28, issue.22, pp.1-12, 2012.
DOI : 10.1371/journal.pone.0037716.s006

S. Mei and H. Zhu, AdaBoost Based Multi-Instance Transfer Learning for Predicting Proteome-Wide Interactions between Salmonella and Human Proteins, PLoS ONE, vol.8, issue.10, pp.1-12, 2014.
DOI : 10.1371/journal.pone.0110488.s008

E. Merelli, G. Armano, N. Cannata, F. Corradini, M. Inverno et al., , 2007.

, Agents in bioinformatics, computational and systems biology, Briefings in Bioinformatics, vol.8, issue.1, p.45

J. Mestres, E. Gregori-puigjane, S. Valverde, and R. V. Sole, Data completeness???the Achilles heel of drug-target networks, Nature Biotechnology, vol.11, issue.9, p.983, 2008.
DOI : 10.1038/nrd892

N. J. Mglincy and N. T. Ingolia, Transcriptome-wide measurement of translation by ribosome profiling, Methods, vol.126, pp.112-129, 2017.
DOI : 10.1016/j.ymeth.2017.05.028

A. M. Michel, K. R. Choudhury, A. E. Firth, N. T. Ingolia, J. F. Atkins et al., Observation of dually decoded regions of the human genome using ribosome profiling data, Genome Research, vol.22, issue.11, pp.2219-2229, 2012.
DOI : 10.1101/gr.133249.111

U. Midic, A. K. Dunker, and Z. Obradovic, Improving protein secondarystructure prediction by predicting ends of secondary-structure segments, Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB'05. Proceedings of the 2005 IEEE Symposium on, pp.1-8, 2005.

M. Miranda, I. Lynce, and V. Manquinho, Inferring phylogenetic trees using pseudo-Boolean optimization, AI Commun, vol.27, issue.3, pp.229-243, 2014.

S. Mitra, S. Datta, T. Perkins, and G. Michailidis, Introduction to machine learning and bioinformatics, 2008.

T. Miyahara and T. Kuboyama, Learning of Glycan Motifs Using Genetic Programming and Various Fitness Functions, Journal of Advanced Computational Intelligence and Intelligent Informatics, vol.18, issue.3, pp.401-408, 2014.
DOI : 10.20965/jaciii.2014.p0401

M. Moll, D. Schwarz, and L. E. Kavraki, Roadmap methods for protein folding, Protein Structure Prediction, pp.219-239, 2008.
DOI : 10.1385/1-59745-574-1:219
URL : http://www.cs.rice.edu/~mmoll/publications/moll2007roadmap-methods-for-protein-folding.pdf

P. T. Monteiro, D. Ropers, R. Mateescu, A. T. Freitas, and H. De-jong, Temporal logic patterns for querying dynamic models of cellular interaction networks, Bioinformatics, vol.20, issue.4, pp.24-227, 2008.
DOI : 10.1038/nbt0402-370
URL : https://hal.archives-ouvertes.fr/inria-00260980

J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, and A. Tramontano, Critical assessment of methods of protein structure prediction (CASP)-Round XII, Proteins: Structure, Function, and Bioinformatics, vol.45, issue.D1, pp.7-15, 2018.
DOI : 10.1093/nar/gkw1132

S. Muggleton, R. D. King, and M. J. Stenberg, Protein secondary structure prediction using logic-based machine learning, "Protein Engineering, Design and Selection", vol.5, issue.7, pp.647-657, 1992.
DOI : 10.1093/protein/5.7.647

J. Muller, C. J. Creevey, J. D. Thompson, D. Arendt, and P. Bork, AQUA: automated quality improvement for multiple sequence alignments, Bioinformatics, vol.25, issue.9, pp.263-265, 2010.
DOI : 10.1093/bioinformatics/btp033
URL : https://academic.oup.com/bioinformatics/article-pdf/26/2/263/553324/btp651.pdf

C. J. Mungall, C. Torniai, G. V. Gkoutos, S. E. Lewis, and M. A. Haendel, Uberon, an integrative multi-species anatomy ontology, Genome Biology, vol.13, issue.1, p.5, 2012.
DOI : 10.1186/1471-2105-5-8
URL : https://genomebiology.biomedcentral.com/track/pdf/10.1186/gb-2012-13-1-r5?site=genomebiology.biomedcentral.com

M. Mushthofa, G. Torres, Y. Van-de-peer, K. Marchal, D. Cock et al., , 2014.

, ASP-G: an ASP-based method for finding attractors in genetic regulatory networks, Bioinformatics, vol.30, issue.21, pp.3086-3092

S. Nagi, D. K. Bhattacharyya, and J. K. Kalita, Complex detection from PPI data using ensemble method, Network Modeling Analysis in Health Informatics and Bioinformatics, vol.8, issue.Suppl 5, p.3, 2017.
DOI : 10.1145/2382936.2383023

A. W. Naik, J. D. Kangas, D. P. Sullivan, M. , and R. F. , Active machine learning-driven experimentation to determine compound effects on protein patterns. eLife, p.10047, 2016.

A. Naldi, J. Carneiro, C. Chaouiya, and D. Thieffry, Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling, PLoS Computational Biology, vol.212, issue.9, pp.1-16, 2010.
DOI : 10.1371/journal.pcbi.1000912.s003
URL : https://hal.archives-ouvertes.fr/inserm-00704876

D. N. Nesbeth, A. Zaikin, Y. Saka, M. C. Romano, C. V. Giuraniuc et al., Synthetic biology routes to bio-artificial intelligence, Essays In Biochemistry, vol.60, issue.4, pp.381-391, 2016.
DOI : 10.1042/EBC20160014

A. Newman and M. Ruhl, Combinatorial Problems on Strings with Applications to Protein Folding, Theoretical Informatics, pp.369-378, 2004.
DOI : 10.1007/978-3-540-24698-5_41

C. Ng, C. Li, F. , and X. , A Fast Algorithm for Reconstructing Multiple Sequence Alignment and Phylogeny Simultaneously, Current Bioinformatics, vol.12, issue.4, pp.329-348, 2017.
DOI : 10.2174/1574893611666161008194345

B. Offmann, M. Tyagi, and A. G. De-brevern, Local Protein Structures, Current Bioinformatics, vol.2, issue.3, pp.165-202, 2007.
DOI : 10.2174/157489307781662105
URL : https://hal.archives-ouvertes.fr/inserm-00175058

O. Okun and H. Priisalu, Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues, Pattern Recognition and Image Analysis, pp.483-490, 2007.
DOI : 10.1007/978-3-540-72849-8_61

M. Omar, R. Salam, R. Abdullah, and N. Rashid, Multiple sequence alignment using optimization algorithms, International Journal of Computational Intelligence, vol.1, issue.2, pp.81-89, 2005.

A. Pandini, A. Fornili, and J. Kleinjung, Structural alphabets derived from attractors in conformational space, BMC Bioinformatics, vol.11, issue.1, p.97, 2010.
DOI : 10.1186/1471-2105-11-97
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-11-97

Y. R. Park, J. Kim, H. W. Lee, Y. J. Yoon, K. et al., GOChase-II: correcting semantic inconsistencies from Gene Ontology-based annotations for gene products, BMC Bioinformatics, vol.12, issue.Suppl 1, p.40, 2011.
DOI : 10.1186/1471-2105-12-S1-S40
URL : https://doi.org/10.1186/1471-2105-12-s1-s40

E. Pashaei and N. Aydin, Frequency difference based DNA encoding methods in human splice site recognition, 2017 International Conference on Computer Science and Engineering (UBMK), pp.586-591, 2017.
DOI : 10.1109/UBMK.2017.8093471

E. Pashaei, M. Ozen, and N. Aydin, Splice sites prediction of human genome using AdaBoost, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp.300-303, 2016.
DOI : 10.1109/BHI.2016.7455894

E. Pashaei, M. Ozen, and N. Aydin, Splice site identification in human genome using random forest, Health and Technology, vol.2, issue.1, pp.141-152, 2017.
DOI : 10.1371/journal.pone.0118432

E. Pashaei, A. Yilmaz, and N. Aydin, A combined SVM and Markov model approach for splice site identification, 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), pp.200-204, 2016.
DOI : 10.1109/ICCKE.2016.7802140

J. Peng and J. Xu, Low-homology protein threading, Bioinformatics, vol.58, issue.7, pp.294-300, 2010.
DOI : 10.1002/prot.20308
URL : https://academic.oup.com/bioinformatics/article-pdf/26/12/i294/795711/btq192.pdf

S. Pérez, A. Sarkar, A. Rivet, C. Breton, and A. Imberty, Glyco3D: A Portal for Structural Glycosciences, Glycoinformatics, pp.241-258, 2015.
DOI : 10.1007/978-1-4939-2343-4_18

M. Pertea, G. M. Pertea, C. M. Antonescu, T. Chang, J. T. Mendell et al., StringTie enables improved reconstruction of a transcriptome from RNA-seq reads, Nature Biotechnology, vol.16, issue.3, pp.290-295, 2015.
DOI : 10.1287/moor.16.2.351

B. Pes, N. Dessì, and M. Angioni, Exploiting the ensemble paradigm for stable feature selection: A case study on high-dimensional genomic data, Information Fusion, vol.35, pp.132-147, 2017.
DOI : 10.1016/j.inffus.2016.10.001

R. Petegrosso, S. Park, T. H. Hwang, and R. Kuang, Transfer learning across ontologies for phenome???genome association prediction, Bioinformatics, vol.11, issue.1, pp.529-536, 2017.
DOI : 10.1109/ICDM.2013.58
URL : https://academic.oup.com/bioinformatics/article-pdf/33/4/529/25146750/btw649.pdf

Y. Piao, M. Piao, K. Park, and K. H. Ryu, An ensemble correlationbased gene selection algorithm for cancer classification with gene expression data, Bioinformatics, issue.24, pp.283306-3315, 2012.
DOI : 10.1093/bioinformatics/bts602
URL : https://academic.oup.com/bioinformatics/article-pdf/28/24/3306/678088/bts602.pdf

M. J. Pietal, J. M. Bujnicki, and L. P. Kozlowski, GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function, Bioinformatics, vol.30, issue.21, pp.313499-3505, 2015.
DOI : 10.1093/bioinformatics/btr220
URL : https://academic.oup.com/bioinformatics/article-pdf/31/21/3499/17122233/btv390.pdf

Y. Pirola, R. Rizzi, E. Picardi, G. Pesole, D. Vedova et al., PIntron: a fast method for detecting the gene structure due to alternative splicing via maximal pairings of a pattern and a text, BMC Bioinformatics, vol.13, issue.Suppl 5, p.2, 2012.
DOI : 10.1136/bmj.308.6943.1552

P. Pokarowski, A. Kolinski, and J. Skolnick, A Minimal Physically Realistic Protein-Like Lattice Model: Designing an Energy Landscape that Ensures All-Or-None Folding to a Unique Native State, Biophysical Journal, vol.84, issue.3, pp.1518-1526, 2003.
DOI : 10.1016/S0006-3495(03)74964-9

V. Popic, R. Salari, I. Hajirasouliha, D. Kashef-haghighi, R. B. West et al., Fast and scalable inference of multi-sample cancer lineages, Genome Biology, vol.10, issue.1, p.91, 2015.
DOI : 10.1137/0207024
URL : https://genomebiology.biomedcentral.com/track/pdf/10.1186/s13059-015-0647-8

L. J. Post, M. Roos, M. S. Marshall, R. Van-driel, and T. M. Breit, A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data, Bioinformatics, vol.18, issue.8, pp.233080-3087, 2007.
DOI : 10.1093/bioinformatics/18.8.1149
URL : https://academic.oup.com/bioinformatics/article-pdf/23/22/3080/547764/btm461.pdf

A. B. Raies and V. B. Bajic, toxicology: computational methods for the prediction of chemical toxicity, Wiley Interdisciplinary Reviews: Computational Molecular Science, vol.125, issue.469, pp.147-172, 2016.
DOI : 10.1093/toxsci/kfr248

J. J. Ramsden, Bioinformatics: An Introduction, Computational Biology, vol.3, 2004.
DOI : 10.1007/978-1-4020-2950-9

U. Rannug, M. Sjögren, A. Rannug, M. Gillner, R. Toftgård et al., Use of artificial intelligence in structure?affinity correlations of 2 (TCDD) receptor ligands, Carcinogenesis, vol.37, issue.11, pp.8-122007, 1991.

R. Ranzinger, K. F. Aoki-kinoshita, M. P. Campbell, S. Kawano, T. Lütteke et al., GlycoRDF: an ontology to standardize glycomics data in RDF, Bioinformatics, vol.28, issue.6, pp.31919-925, 2015.
DOI : 10.1093/nar/28.1.10

I. Reid, N. O-'toole, O. Zabaneh, R. Nourzadeh, M. Dahdouli et al., , 2014.

, Snowyowl: accurate prediction of fungal genes by using rna-seq and homology information to select among ab initio models, BMC Bioinformatics, vol.15, issue.1, p.229

D. Reker, P. Schneider, and G. Schneider, Multi-objective active machine learning rapidly improves structure???activity models and reveals new protein???protein interaction inhibitors, Chemical Science, vol.38, issue.6, pp.3919-3927, 2016.
DOI : 10.1016/j.jmgm.2012.06.010

D. Reker, P. Schneider, G. Schneider, and J. Brown, Active learning for computational chemogenomics, Future Medicinal Chemistry, vol.44, issue.1, pp.381-402, 2017.
DOI : 10.1002/anie.201400988

J. I. Requeno and J. M. Colom, Evaluation of properties over phylogenetic trees using stochastic logics, BMC Bioinformatics, vol.12, issue.10, p.235, 2016.
DOI : 10.1101/gr.361602

J. I. Requeno, G. De-miguel-casado, R. Blanco, C. , and J. M. , Temporal Logics for Phylogenetic Analysis via Model Checking, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, issue.4, pp.1058-1070, 2013.
DOI : 10.1109/TCBB.2013.87

F. Rinaldi, O. Lithgow, S. Gama-castro, H. Solano, A. López-fuentes et al., Strategies towards digital and semi-automated curation in RegulonDB, Database, p.12, 2017.

M. P. Robertson and G. F. Joyce, The Origins of the RNA World, Cold Spring Harbor Perspectives in Biology, vol.4, issue.5, p.3608, 2012.
DOI : 10.1101/cshperspect.a003608

C. Rodríguez-penagos, H. Salgado, I. Martínez-flores, and J. Collado-vides, Automatic reconstruction of a bacterial regulatory network using Natural Language Processing, BMC Bioinformatics, vol.8, issue.1, p.293, 2007.
DOI : 10.1186/1471-2105-8-293

A. Röhl and A. Bockmayr, A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks, BMC Bioinformatics, vol.103, issue.1, 2017.
DOI : 10.1016/j.biosystems.2010.09.011

B. Rost and C. Sander, Prediction of Protein Secondary Structure at Better than 70% Accuracy, Journal of Molecular Biology, vol.232, issue.2, pp.584-599, 1993.
DOI : 10.1006/jmbi.1993.1413

Y. Sakiyama, H. Yuki, T. Moriya, K. Hattori, M. Suzuki et al., Predicting human liver microsomal stability with machine learning techniques, Journal of Molecular Graphics and Modelling, vol.26, issue.6, pp.907-915, 2008.
DOI : 10.1016/j.jmgm.2007.06.005

R. Samaga, A. V. Kamp, and S. Klamt, Computing Combinatorial Intervention Strategies and Failure Modes in Signaling Networks, Journal of Computational Biology, vol.17, issue.1, pp.39-53, 2010.
DOI : 10.1089/cmb.2009.0121

I. N. Sarkar, Mining the Bibliome, Translational Informatics, pp.75-96, 2015.
DOI : 10.1007/978-1-4471-4646-9_5

R. Sawada, M. Kotera, Y. , and Y. , Benchmarking a Wide Range of Chemical Descriptors for Drug-Target Interaction Prediction Using a Chemogenomic Approach, Molecular Informatics, vol.28, pp.11-12719, 2014.
DOI : 10.1093/bioinformatics/bts413

, Jacques Nicolas

L. Schietgat, C. Vens, J. Struyf, H. Blockeel, D. Kocev et al., Predicting gene function using hierarchical multi-label decision tree ensembles, BMC Bioinformatics, vol.11, issue.1, 2010.
DOI : 10.1186/1471-2105-11-2

S. Schroedl, An Improved Search Algorithm for Optimal Multiple-Sequence Alignment, Journal of Artificial Intelligence Research, vol.23, pp.587-623, 2005.
DOI : 10.1613/jair.1534

C. Schulte and P. J. Stuckey, Efficient constraint propagation engines, ACM Transactions on Programming Languages and Systems, vol.31, issue.1, pp.1-2, 2008.
DOI : 10.1145/1452044.1452046

R. Schwartz and A. A. Schäffer, The evolution of tumour phylogenetics: principles and practice, Nature Reviews Genetics, vol.71, issue.4, p.213, 2017.
DOI : 10.1038/nature13952

R. S. Senger and M. N. Karim, Prediction of N-linked glycan branching patterns using artificial neural networks, Mathematical Biosciences, vol.211, issue.1, pp.89-104, 2008.
DOI : 10.1016/j.mbs.2007.10.005

Z. Shao, Y. Hirayama, Y. Yamanishi, and H. Saigo, Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure???Activity Relationship (QSAR) Models, Journal of Chemical Information and Modeling, vol.55, issue.12, pp.2519-2527, 2015.
DOI : 10.1021/acs.jcim.5b00376

S. Shatabda, M. A. Newton, and A. Sattar, Constraint-Based Evolutionary Local Search for Protein Structures with Secondary Motifs, PRICAI 2014: Trends in Artificial Intelligence, pp.333-344, 2014.
DOI : 10.1007/978-3-319-13560-1_27

D. L. Shaw, A. S. Islam, M. S. Rahman, and M. Hasan, Protein folding in HP model on hexagonal lattices with diagonals, BMC Bioinformatics, vol.15, issue.Suppl 2, p.7, 2014.
DOI : 10.1007/BF01303193

T. Sheela and L. Rangarajan, Combination of Feature Selection Methods for??the Effective Classification of Microarray Gene Expression Data, Recent Trends in Image Processing and Pattern Recognition: First International Conference, pp.137-145, 2016.
DOI : 10.1109/MIS.2009.92

R. Sherhod, P. N. Judson, T. Hanser, J. D. Vessey, S. J. Webb et al., Emerging Pattern Mining To Aid Toxicological Knowledge Discovery, Journal of Chemical Information and Modeling, vol.54, issue.7, pp.541864-1879, 2014.
DOI : 10.1021/ci5001828

P. Shvaiko and J. Euzenat, Ontology Matching: State of the Art and Future Challenges, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.1, pp.158-176, 2013.
DOI : 10.1109/TKDE.2011.253
URL : https://hal.archives-ouvertes.fr/hal-00917910

G. B. Singh, Introduction to Bioinformatics, In Fundamentals of Bioinformatics and Computational Biology, pp.3-10, 2015.
DOI : 10.1007/978-3-319-11403-3_1

H. Singh, S. Singh, R. , and G. P. , Evaluation of Protein Dihedral Angle Prediction Methods, PLoS ONE, vol.29, issue.8, pp.1-9, 2014.
DOI : 10.1371/journal.pone.0105667.s002

M. J. Skwark, D. Raimondi, M. Michel, and A. Elofsson, Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns, PLoS Computational Biology, vol.30, issue.11, pp.101-115, 2014.
DOI : 10.1371/journal.pcbi.1003889.s002

B. Smith, M. Ashburner, C. Rosse, J. Bard, W. Bug et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration, Nature Biotechnology, vol.10, issue.11, pp.251251-1255, 2007.
DOI : 10.1093/bioinformatics/btm155

S. K. Smitha and S. N. Reddy, Amyloid motif prediction using ensemble approach, Current Bioinformatics, vol.11, issue.3, pp.357-365, 2016.

J. Song, K. Burrage, Z. Yuan, and T. Huber, Prediction of cis/trans isomerization in proteins using psi-blast profiles and secondary structure information, BMC Bioinformatics, vol.7, issue.1, p.124, 2006.
DOI : 10.1186/1471-2105-7-124

W. W. Soon, M. Hariharan, and M. P. Snyder, High-throughput sequencing for biology and medicine, Molecular Systems Biology, vol.759, issue.1, p.640, 2013.
DOI : 10.1038/ng1891

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, 2015.
DOI : 10.1109/TCBB.2014.2343960

S. Sridhar, F. Lam, G. E. Blelloch, R. Ravi, and R. Schwartz, Mixed Integer Linear Programming for Maximum-Parsimony Phylogeny Inference, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, issue.3, pp.323-331, 2008.
DOI : 10.1109/TCBB.2008.26

M. J. Sternberg, A. Tamaddoni-nezhad, V. I. Lesk, E. Kay, P. G. Hitchen et al., , 2013.

, Gene function hypotheses for the campylobacter jejuni glycome generated by a logic-basedApproach, Journal of Molecular Biology, vol.425, issue.1, pp.186-197

R. Stevens, C. A. Goble, and S. Bechhofer, Ontology-based knowledge representation for bioinformatics, Briefings in Bioinformatics, vol.1, issue.4, pp.398-414, 2000.
DOI : 10.1093/bib/1.4.398

D. Sundfeld, C. Razzolini, G. Teodoro, A. Boukerche, and A. C. De-melo, PA-Star: A disk-assisted parallel A-Star strategy with locality-sensitive hash for multiple sequence alignment, Journal of Parallel and Distributed Com- puting, 2017.
DOI : 10.1016/j.jpdc.2017.04.014

I. Takigawa, K. Hashimoto, M. Shiga, M. Kanehisa, and H. Mamitsuka, Mining patterns from glycan structures, Proceedings of the International Beilstein Symposium on Glyco-Bioinformatics, pp.13-24, 2010.

I. Takigawa and H. Mamitsuka, Graph mining: procedure, application to drug discovery and recent advances, Drug Discovery Today, vol.18, issue.1-2, pp.50-57, 2013.
DOI : 10.1016/j.drudis.2012.07.016

G. The and . Consortium, Expansion of the gene ontology knowledgebase and resources, Nucleic Acids Research, vol.45, issue.D1, pp.331-338, 2017.

M. Tiemeyer, K. Aoki, J. Paulson, R. D. Cummings, W. S. York et al., GlyTouCan: an accessible glycan structure repository, Glycobiology, vol.24, issue.Database issue, pp.27915-919, 2017.
DOI : 10.1093/glycob/cwu018

S. Traoré, K. E. Roberts, D. Allouche, B. R. Donald, I. André et al., Fast search algorithms for computational protein design, Journal of Computational Chemistry, vol.35, issue.12, pp.371048-1058, 2016.
DOI : 10.1002/(SICI)1097-0134(19990501)35:2<133::AID-PROT1>3.0.CO;2-N

P. Traynard, A. Fauré, F. Fages, and D. Thieffry, Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation, Bioinformatics, vol.10, issue.17, pp.32-772, 2016.
DOI : 10.4161/cc.10.18.17350
URL : https://hal.archives-ouvertes.fr/hal-01378465

N. Ueda, K. F. Aoki-kinoshita, A. Yamaguchi, T. Akutsu, and H. Mamitsuka, A probabilistic model for mining labeled ordered trees: capturing patterns in carbohydrate sugar chains, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.8, pp.171051-1064, 2005.
DOI : 10.1109/TKDE.2005.117

W. Ugarte, P. Boizumault, B. Crémilleux, A. Lepailleur, S. Loudni et al., Skypattern mining: From pattern condensed representations to dynamic constraint satisfaction problems, Combining Constraint Solving with Mining and Learning, pp.48-69, 2017.
DOI : 10.1016/j.artint.2015.04.003
URL : https://hal.archives-ouvertes.fr/hal-02048224

M. Vendruscolo, E. Kussell, and E. Domany, Recovery of protein structure from contact maps. Folding and Design, pp.295-306, 1997.

G. Verfaillie and N. Jussien, Constraint Solving in Uncertain and Dynamic Environments: A Survey, Constraints, vol.10, issue.5, pp.253-281, 2005.
DOI : 10.1007/s10601-005-2239-9
URL : https://hal.archives-ouvertes.fr/hal-00293900

J. Vert and L. Jacob, Machine Learning for In Silico Virtual Screening and Chemical Genomics: New Strategies, Combinatorial Chemistry & High Throughput Screening, vol.11, issue.8, pp.677-685, 2008.
DOI : 10.2174/138620708785739899
URL : https://hal.archives-ouvertes.fr/hal-00433569

S. Videla, C. Guziolowski, F. Eduati, S. Thiele, M. Gebser et al., Learning Boolean logic models of signaling networks with ASP, Advances in Computational Methods in Systems Biology, pp.59979-101, 2015.
DOI : 10.1016/j.tcs.2014.06.022
URL : https://hal.archives-ouvertes.fr/hal-01058610

S. Videla, J. Saez-rodriguez, C. Guziolowski, and A. Siegel, caspo: a toolbox for automated reasoning on the response of logical signaling networks families, Bioinformatics, vol.9, issue.6, pp.33947-950, 2017.
DOI : 10.1088/1478-3975/9/5/055001
URL : https://hal.archives-ouvertes.fr/hal-01426880

A. Von-kamp and S. Klamt, Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks, PLoS Computational Biology, vol.17, issue.1, p.1003378, 2014.
DOI : 10.1371/journal.pcbi.1003378.t003

R. Wald, T. M. Khoshgoftaar, D. Dittman, W. Awada, and A. Napolitano, , 2012.

, An extensive comparison of feature ranking aggregation techniques in bioinformatics, Information Reuse and Integration (IRI), 2012 IEEE 13th International Conference on, pp.377-384

S. I. Walker and P. C. Davies, The algorithmic origins of life, Journal of The Royal Society Interface, vol.75, issue.6519, p.1020120869, 2013.
DOI : 10.1016/0167-2789(94)90273-9

S. Wan, M. Mak, and S. Kung, HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins, PLoS ONE, vol.26, issue.200, pp.1-12, 2014.
DOI : 10.1371/journal.pone.0089545.t003

S. Wang, W. Li, S. Liu, and J. Xu, RaptorX-Property: a web server for protein structure property prediction, Nucleic Acids Research, vol.2015, issue.W1, pp.430-435, 2016.
DOI : 10.1093/nar/gkh429

S. Wang, W. Li, R. Zhang, S. Liu, and J. Xu, CoinFold: a web server for protein contact prediction and contact-assisted protein folding, Nucleic Acids Research, vol.2015, issue.W1, pp.44-361, 2016.
DOI : 10.1371/journal.pcbi.1004661

S. Wang, J. Peng, J. Ma, and J. Xu, Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields, Scientific Reports, vol.45, issue.1, 2016.
DOI : 10.1093/nar/gkm895

S. Wang, S. Sun, Z. Li, R. Zhang, and J. Xu, Accurate de novo prediction of protein contact map by ultra-deep learning model, PLOS Computational Biology, vol.13, issue.1, pp.1-34, 2017.

X. Wang, Z. Xuan, X. Zhao, Y. Li, and M. Q. Zhang, High-resolution human core-promoter prediction with CoreBoost_HM, Genome Research, vol.19, issue.2, pp.266-275, 2009.
DOI : 10.1101/gr.081638.108

Y. Wang, H. Mao, Y. , and Z. , Protein secondary structure prediction by using deep learning method. Knowledge-Based Systems, pp.115-123, 2017.

Z. Wang and J. Xu, Predicting protein contact map using evolutionary and physical constraints by integer programming, Bioinformatics, vol.71, issue.6, pp.29-266, 2013.
DOI : 10.1002/prot.21813

D. Wei, H. Zhang, Y. Wei, and Q. Jiang, A novel splice site prediction method using support vector machine, Journal of Computational Information Systems, vol.9, issue.20, pp.8053-8060, 2013.

K. Wei, R. Iyer, and J. Bilmes, Submodularity in Data Subset Selection and Active Learning, Proceedings of the 32nd International Conference on Machine Learning of Proceedings of Machine Learning Research, pp.1954-1963, 2015.

P. L. Whetzel, N. F. Noy, N. H. Shah, P. R. Alexander, C. Nyulas et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications, Nucleic Acids Research, vol.140, issue.1, pp.39-541, 2011.
DOI : 10.1104/pp.105.063008

B. Wu, T. Abbott, D. Fishman, W. Mcmurray, G. Mor et al., Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data, Bioinformatics, vol.19, issue.13, pp.191636-1643, 2003.
DOI : 10.1093/bioinformatics/btg210

G. Wu, J. You, L. , and G. , Quartet-based phylogeny reconstruction with answer set programming, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.4, issue.1, 2007.

Q. Wuyun, W. Zheng, Z. Peng, Y. , and J. , A large-scale comparative assessment of methods for residue???residue contact prediction, Briefings in Bioinformatics, vol.61, issue.7, p.106, 2016.
DOI : 10.1093/bioinformatics/btw181

J. Xia, M. Wu, Z. You, X. Zhao, L. et al., Prediction of &#946;-Hairpins in Proteins Using Physicochemical Properties and Structure Information, Protein & Peptide Letters, vol.17, issue.9, pp.1123-1128, 2010.
DOI : 10.2174/092986610791760333

Y. Xiao and E. R. Dougherty, The impact of function perturbations in Boolean networks, Bioinformatics, vol.1, issue.19, pp.1265-1273, 2007.
DOI : 10.1016/S1535-6108(02)00045-4

L. C. Xue, J. A. Rodrigues, D. Dobbs, V. Honavar, and A. M. Bonvin, , 2017.

, Template-based protein?protein docking exploiting pairwise interfacial residue restraints, Briefings in Bioinformatics, vol.18, issue.3, pp.458-466

Y. Yamanishi, F. Bach, and J. Vert, Glycan classification with tree kernels, Bioinformatics, vol.17, issue.suppl_1, pp.1211-1216, 2007.
DOI : 10.1109/TKDE.2005.117
URL : https://hal.archives-ouvertes.fr/hal-00433576

N. Yanev, M. Traykov, P. Milanov, Y. , and B. , Protein Folding Prediction in a Cubic Lattice in Hydrophobic-Polar Model, Journal of Computational Biology, vol.24, issue.5, pp.412-421, 2017.
DOI : 10.1089/cmb.2016.0181

P. Yang, J. W. Ho, Y. H. Yang, and B. B. Zhou, Gene-gene interaction filtering with ensemble of filters, BMC Bioinformatics, vol.12, issue.Suppl 1, p.10, 2011.
DOI : 10.1186/1471-2105-12-S1-S10

P. Yang, S. J. Humphrey, D. E. James, Y. H. Yang, and R. Jothi, Positiveunlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data, Bioinformatics, issue.2, pp.32252-259, 2016.

P. Yang, H. Yang, Y. Zhou, B. , Y. Zomaya et al., A Review of Ensemble Methods in Bioinformatics, Current Bioinformatics, vol.5, issue.4, pp.296-308, 2010.
DOI : 10.2174/157489310794072508

Y. Yang, J. Gao, J. Wang, R. Heffernan, J. Hanson et al., Sixty-five years of the long march in protein secondary structure prediction: the final stretch?, Briefings in Bioinformatics, vol.18, issue.1, p.129, 2016.
DOI : 10.1093/bioinformatics/btw678

Y. Yang, J. Gao, J. Wang, R. Heffernan, J. Hanson et al., Sixty-five years of the long march in protein secondary structure prediction: the final stretch?, Briefings in Bioinformatics, vol.18, issue.1, p.129, 2016.
DOI : 10.1093/bioinformatics/btw678

T. Yoshizumi, T. Miura, and T. Ishida, A* with partial expansion for large branching factor problems, AAAI/IAAI, pp.923-929, 2000.

G. Yuan, L. Cai, M. Elowitz, T. Enver, G. Fan et al., Challenges and emerging directions in single-cell analysis, Genome Biology, vol.525, issue.1, p.84, 2017.
DOI : 10.1038/525306a

T. Zhang, E. Faraggi, Z. Li, and Y. Zhou, Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation, Cell Biochemistry and Biophysics, vol.26, issue.Suppl 1, pp.1193-1205, 2013.
DOI : 10.1093/bioinformatics/btq373

Y. Zhang and J. C. Rajapakse, Machine learning in bioinformatics, 2009.
DOI : 10.1002/9780470397428

Y. Zhao, M. Hayashida, and T. Akutsu, Integer programming-based method for grammar-based tree compression and its application to pattern extraction of glycan tree structures, BMC Bioinformatics, vol.11, issue.Suppl 11, pp.11-15, 2010.
DOI : 10.1186/1471-2105-11-S11-S4

Y. Zhao, M. Hayashida, Y. Cao, J. Hwang, and T. Akutsu, Grammar-based compression approach to extraction of common rules among multiple trees of glycans and RNAs, BMC Bioinformatics, vol.1, issue.5, p.128, 2015.
DOI : 10.1109/TCBB.2004.11

R. Zhou and E. A. Hansen, Space-efficient memory-based heuristics, AAAI, pp.677-682, 2004.