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Christine SINOQUET

ENSEIGNANT-CHERCHEUR

HDR

: Christine.Sinoquetatls2n.fr

Page pro : http://christinesinoquet.wixsite.com/christinesinoquet

Adresse :

Université de Nantes - faculté des Sciences et Techniques ( FST )
Petit Port
2 Chemin de la Houssinière
BP 92208
44322 Nantes Cedex 3

Batiment 11, étage 1, bureau 109


Publications référencées sur HAL

Revues internationales avec comité de lecture (ART_INT)

    • [1] C. Sinoquet. A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies. In BMC Bioinformatics ; éd. BioMed Central, 2018, vol. 19, num. 1.
      https://hal.science/hal-01984726
    • [2] C. Niel, C. Sinoquet, C. Dina, G. Rocheleau. SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS. In Bioinformatics ; éd. Oxford University Press (OUP), 2018, vol. 34, num. 16.
      https://hal.science/hal-01986668
    • [3] C. Niel, C. Sinoquet, C. Dina, G. Rocheleau. A survey about methods dedicated to epistasis detection. In Frontiers in Genetics ; éd. Frontiers Media, 2015, vol. 6, num. Article 285.
      https://hal.science/hal-01205577
    • [4] R. Mourad, C. Sinoquet, N. Zhang, T. Liu, P. Leray. A survey on latent tree models and applications. In Journal of Artificial Intelligence Research ; éd. Association for the Advancement of Artificial Intelligence, 2013, vol. 47.
      https://hal.science/hal-00828445
    • [5] R. Mourad, C. Sinoquet, P. Leray. Probabilistic graphical models for genetic association studies. In Briefings in Bioinformatics ; éd. Oxford University Press (OUP), 2012, vol. 13, num. 1.
      https://hal.science/hal-00573325
    • [6] V. Perduca, C. Sinoquet, R. Mourad, G. Nuel. Alternative Methods for H1 Simulations in Genome-Wide Association Studies. In Human Heredity ; éd. Karger, 2012, vol. 73, num. 2.
      https://hal.science/hal-00686364
    • [7] R. Mourad, C. Sinoquet, C. Dina, P. Leray. Visualization of pairwise and multilocus linkage disequilibrium structure using latent forests.. In PLoS ONE ; éd. Public Library of Science, 2011, vol. 6, num. 12.
      https://hal.science/hal-00655876
    • [8] R. Mourad, C. Sinoquet, P. Leray. A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies.. In BMC Bioinformatics ; éd. BioMed Central, 2011, vol. 12, num. 1.
      https://hal.science/hal-00567988
    • [9] J. Ahmad, J. Bourdon, D. Eveillard, J. Fromentin, O. Roux, C. Sinoquet. Temporal constraints of a gene regulatory network: Refining a qualitative simulation.. In BioSystems ; éd. Elsevier, 2009, vol. 98, num. 3.
      https://hal.science/hal-00415921
    • [10] J. Ahmad, J. Bourdon, D. Eveillard, J. Fromentin, O. Roux, C. Sinoquet. Temporal constraints of a gene regulatory network: refining a qualitative simulation. In BioSystems ; éd. Elsevier, 2009.
      https://hal.science/hal-00423353
    • [11] C. Sinoquet. Iterative two-pass algorithm for missing data imputation in SNP arrays. In Journal of Bioinformatics and Computational Biology ; éd. World Scientific Publishing, 2009, vol. 7, num. 5.
      https://hal.science/hal-00423339
    • [12] C. Sinoquet, S. Demey, F. Braun. Large-scale computational and statistical analyses of high transcription potentialities in 32 prokaryotic genomes. In Nucleic Acids Research ; éd. Oxford University Press, 2008, vol. 36, num. 10.
      https://hal.science/hal-00423408
    • [13] A. Berry, A. Sigayret, C. Sinoquet. Maximal sub-triangulation in preprocessing phylogenetic data. In Soft Computing ; éd. Springer Verlag, 2006, vol. 10, num. 5.
      https://hal.science/hal-00423418

Conférences internationales avec comité de lecture et actes (COMM_INT)

    • [14] F. Dama, C. Sinoquet. Prediction and Inference in a Partially Hidden Markov-switching Framework with Autoregression. Application to Machinery Health Diagnosis.. In 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), novembre 2021, virtual event, états-Unis.
      https://hal.science/hal-03345260
    • [15] H. Boisaubert, C. Sinoquet. Detection of gene-gene interactions: methodological comparison on real-world data and insights on synergy between methods.. In International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC2019, février 2019, Prague, République tchèque.
      https://hal.science/hal-01986665
    • [16] C. Sinoquet, C. Niel. Enhancement of a stochastic Markov blanket framework with ant colony optimization, to uncover epistasis in genetic association studies.. In ESANN 2018 - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, avril 2018, Bruges, Belgique.
      https://hal.science/hal-01984753
    • [17] C. Sinoquet, K. Mekhnacha. Combining latent tree modeling with a random forest-based approach, for genetic association studies. In 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN2018, avril 2018, Bruges, Belgique.
      https://hal.science/hal-01984665
    • [18] C. Niel, C. Sinoquet. Enhanced ensemble approach to learn Markov blankets for feature subset selection in high-dimensional settings. Illustration with an application to mine genetic data.. In Cap2018 (French Conference on Machine Learning), juin 2018, Rouen, France.
      https://hal.science/hal-01986670
    • [19] C. Sinoquet, C. Niel. Ant colony optimization for Markov blanket-based feature selection. Application for precision medicine. In 4th International Conference on Machine Learning, Optimization, and Data Science, LOD2018, septembre 2018, Volterra, Tuscany, Italie.
      https://hal.science/hal-01986649
    • [20] C. Sinoquet, K. Mekhnacha. Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data.. In IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA2018, octobre 2018, Turin, Italie.In F. Bonchi, F. Provost, T. Eliassi-Rad, W. Wang, C. Cattuto, R. Ghani (eds.) (éds.), . , 2018.
      https://hal.science/hal-01986653
    • [21] C. Sinoquet, K. Mekhnacha. Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.. In 17th International Symposium on Intelligent Data Analysis, IDA2018, octobre 2018, 's-Hertogenbosch, Pays-Bas.In W. Duivesteijn, A. Siebes, A. Ukkonen (eds.) (éds.), . Springer, 2018.
      https://hal.science/hal-01986660
    • [22] D. Phan, P. Leray, C. Sinoquet. Modeling genetical data with forests of latent trees for applications in association genetics at a large scale. Which clustering method should be chosen?. In International Conference on Bioinformatics Models, Methods and Algorithms, Bioinformatics2015, novembre 2014, Lisbon, Portugal.
      https://hal.science/hal-01084907
    • [23] V. Perduca, R. Mourad, C. Sinoquet, G. Nuel. Simulation of phenotypes under H1 in genome wide association studies and applications.. In Fourth edition of workshop Statistical Methods for Post Genomics Analysis, SMPGD2012, France, Lyon, january 26-27, janvier 2012, Lyon, France.
      https://hal.science/hal-00915541
    • [24] C. Sinoquet, R. Mourad, P. Leray. Forests of latent tree models for the detection of genetic associations. In International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2012), février 2012, Vilamoura, Portugal.
      https://hal.science/hal-00637500
    • [25] V. Perduca, R. Mourad, C. Sinoquet, G. Nuel. Waffect: a method to simulate case-control samples in genome-wide association studies.. In JOBIM, juin 2011, Paris, France.
      https://hal.science/hal-01986673
    • [26] R. Mourad, C. Sinoquet, P. Leray. Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies. In COMPSTAT, Nineteenth International Conference on Computational Statististics, août 2010, Paris, France.
      https://hal.science/hal-00484696
    • [27] C. Sinoquet. SNPShuttle: bi-directional scan of SNP arrays to gain accuracy in missing genotype inference. In Proc. Seventh Asia-Pacific Bioinformatics Conference, APBC2009, ISBN 978-7-302-19048-6, janvier 2009, Beijing, Chine.In Michael Q.Zhang, Michael S. Waterman and Xuegong Zhang (éds.), Proc. Seventh Asia-Pacific Bioinformatics Conference, APBC2009, ISBN 978-7-302-19048-6. Tsinghua University Press, 2009.
      https://hal.science/hal-00423431
    • [28] G. Blin, G. Fertin, I. Rusu, C. Sinoquet. Extending the Hardness of RNA Secondary Structure Comparison. In 1st International Symposium on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies (ESCAPE 2007), avril 2007, Hangzhou, Chine.In Chen Bo and Paterson Mike and Zhang Guochuan (éds.), Proc. 1st International Symposium on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies (ESCAPE 2007). Springer-Verlag, 2007.
      https://hal.science/hal-00418248
    • [29] C. Sinoquet. A novel approach for structured consensus motif inference under specificity and quorum constraints. In Proc. Fourth Asia-Pacific Bioinformatics Conference, APBC2006, Advances in Bioinformatics and Computational Biology, ISBN 1-86094-623-2, février 2006, Taipei, Taïwan.In Tao Jiang, Ueng-Cheng Yang, Yi-Ping Phoebe Chen and Limsoon Wong (éds.), Proc. Fourth Asia-Pacific Bioinformatics Conference, APBC2006, Advances in Bioinformatics and Computational Biology, ISBN 1-86094-623-2. Imperial College Press, 2006.
      https://hal.science/hal-00423435
    • [30] C. Sinoquet. A cooperative strategy dedicated to structured motif discovery in genomic data. In Proc. Fith ALIO-EURO Conference on Combinatorial Optimization, octobre 2005, Paris, France.In I. Charon and O. Hudry (éds.), Proc. Fith ALIO-EURO Conference on Combinatorial Optimization. , 2005.
      https://hal.science/hal-00423457
    • [31] C. Sinoquet. When chance helps inferring a structured consensus motif from DNA sequences: study of the metaheuristics approach Kaos. In Proc. CompBioNets2005, Algorithms and Computational Methods for Biochemical and Evolutionary Networks, ISBN 1904987311, décembre 2005, Lyon, France.In Marie-France Sagot and Katia Guimaraes (éds.), Proc. CompBioNets2005, Algorithms and Computational Methods for Biochemical and Evolutionary Networks, ISBN 1904987311. College Publications, 2005.
      https://hal.science/hal-00423436
    • [32] C. Sinoquet. Révélation de motif consensus fonctionnel dans un génome. In FRANCORO IV, International French-speaking Conference on Operational Research, octobre 2004, Fribourg, Suisse.
      https://hal.science/hal-00423458
    • [33] A. Berry, A. Sigayret, C. Sinoquet. Maximal sub-triangulation as preprocessing phylogenetic data. In International Conference Journées de l'Informatique Messine, septembre 2003, Metz, France.In E. SanJuan & al. (éds.), Proceedings of JIM'03. INRIA, 2003.
      https://hal.science/hal-00522361
    • [34] C. Sinoquet. Maximal sub-triangulation as preprocessing phylogenetic data. In Proc. Fourth International Conference on Knowledge Discovery and Discrete Mathematics, septembre 2003, Metz, France.In M.Nadif, A. Napoli, E. SanJuan and A. Sigayret (éds.), Proc. Fourth International Conference on Knowledge Discovery and Discrete Mathematics. , 2003.
      https://hal.science/hal-00423456

Conférences nationales avec comité de lecture et actes (COMM_NAT)

    • [35] C. Niel, C. Sinoquet. Optimisation par colonie de fourmis pour la sélection de variables par construction stochastique de couverture de Markov - Application pour la médecine de précision.. In 19th edition of the annual congress of the French Society for Operation Research and Decision Assistance, ROADEF2018, février 2018, Lorient, France.
      https://hal.science/hal-01986671
    • [36] D. Phan, P. Leray, C. Sinoquet. Impact du choix de la méthode de partitionnement pour les forêts d'arbres latents. In SFC2015, septembre 2015, Nantes, France.
      https://hal.science/hal-01205544
    • [37] C. Sinoquet, R. Mourad, P. Leray. Modeling of genotype data with forests of latent trees to detect genetic causes of diseases. In Ado2013 (Machine Learning and Omics Data), décembre 2013, Lille, France.
      https://hal.science/hal-00915538
    • [38] V. Perduca, R. Mourad, C. Sinoquet, G. Nuel. Waffect : a method to simulate case-control samples in genome-wide association studies.. In 43ème édition des journées de la Société Française de Statistique, mai 2011, Gammarth, Tunisie.
      https://hal.science/hal-00915543
    • [39] R. Mourad, C. Sinoquet, P. Leray. Apprentissage de réseaux bayésiens hiérarchiques latents pour les études d'association pangénomiques. In Proc. JFRB 2010, 5th French-speaking meeting on Bayesian networks, Nantes, mai 2010, Nantes, France.
      https://hal.science/hal-00484706
    • [40] R. Mourad, C. Sinoquet, P. Leray. Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiques. In Proc. SFC 2010, XVIIth Join Meeting of the French Society of Classification, France, Saint-Denis de la Réunion, 9-11 june, juin 2010, Saint-Denis de la Réunion, France.
      https://hal.science/hal-00484705
    • [41] R. Mourad, C. Sinoquet, P. Leray. Hierarchical Bayesian networks applied to association genetics. In MODGRAPH 2010 (Modèles graphiques probabilistes pour l'intégration de données hétérogènes et la découverte de modèles causaux en biologie), Journée satellite de JOBIM 2010, septembre 2010, Montpellier, France.
      https://hal.science/hal-00915546
    • [42] R. Mourad, C. Sinoquet, P. Leray. A Bayesian network approach to model local dependencies among SNPs. In MODGRAPH 2009 Probabilistic graphical models for integration of complex data and discovery of causal models in biology, satellite meeting of JOBIM 2009, juin 2009, Nantes, France.
      https://hal.science/hal-00470528
    • [43] R. Mourad, C. Sinoquet, P. Leray. Modélisation des dépendances locales entre SNP à l'aide d'un réseau bayésien. In Proc. SFC'09, XVIth Join Meeting of the French Society of Classification, actes des 16èmes rencontres de la Société Francophone de Classification, septembre 2009, Grenoble, France.In Gérard d'Aubigny (éds.), Proc. SFC'09, XVIth Join Meeting of the French Society of Classification, actes des 16èmes rencontres de la Société Francophone de Classification. , 2009.
      https://hal.science/hal-00423461
    • [44] C. Sinoquet. Reverse translation of amino-acid sequences: a method guided by an n-gram model. In Proc. JOBIM 2000 : Journées Ouvertes pour la Biologie, l'Informatique et les Mathématiques Open workshop for Biology, Computer Science and Mathematics, mai 2000, Montpellier, France.In G. Caraux, O. Gascuel and M.-F. Sagot (éds.), Proc. JOBIM 2000 : Journées Ouvertes pour la Biologie, l'Informatique et les Mathématiques Open workshop for Biology, Computer Science and Mathematics. , 2000.
      https://hal.science/hal-00423459
    • [45] A. Durbec, C. Sinoquet, M. Lafont. Interdépendances nappe-gravières en région Alsace. In Forum scientifique «bravo l'eau», 29 mars 1990, au Conseil Général du Haut-Rhin à Colmar, 1990, , France.
      https://hal.inrae.fr/hal-02609014

Ouvrages - Chapitres d‘ouvrages et directions d‘ouvrages (OUV)

    • [46] H. Boisaubert, C. Sinoquet. Machine learning and combinatorial optimization to detect gene-gene interactions in genome-wide real data: looking through the prism of four methods and two protocols. In Biomedical Engineering Systems and Technologies, 12th International Joint Conference, BIOSTEC2019, Czech Republic, Prague, 22-24 february, Extended Selected Papers, Communication in Computer and Information Science, A. Fred and H. Gamboa (eds.), Springer, 29 pages. 2020
      https://hal.science/hal-02455132
    • [47] D. Phan, P. Leray, C. Sinoquet. Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-wide Association Studies. Which clustering should be chosen?. In Communication in Computer and Information Science. 31-03-2015
      https://hal.science/hal-01204956
    • [48] A. Cliquet Jr, M. Secca, J. Schier, O. Pastor, C. Sinoquet, H. Loose, M. Bienkiewicz, C. Verdier, G. Plantier, T. Schultz, A. Fred, H. Gamboa. Biomedical Engineering Systems and Technologies, 7th International Joint Conference, BIOSTEC2014, Extended Selected Papers. 03-03-2014
      https://hal.science/hal-01169026
    • [49] C. Sinoquet, R. Mourad. Modeling linkage disequilibrium and performing association studies through probabilistic graphical models: a visiting tour of recent advances.. In Probabilistic graphical models for genetics, genomics, and postgenomics. 18-09-2014
      https://hal.science/hal-01168755
    • [50] C. Sinoquet. Essentials to understand probabilistic graphical models: a tutorial about inference and learning.. In Probabilistic graphical models for genetics, genomics, and postgenomics. 18-09-2014
      https://hal.science/hal-01168741
    • [52] C. Sinoquet. Probabilistic graphical models for next-generation genomics and genetics.. In Probabilistic graphical models for genetics, genomics, and postgenomics. 18-09-2014
      https://hal.science/hal-01166517
    • [53] C. Sinoquet, R. Mourad, P. Leray. Forests of latent tree models to decipher genotype-phenotype associations. In Biomedical Engineering Systems and Technologies, Communication in Computer and Information Science 357. 01-01-2013
      https://hal.science/hal-00915532
    • [54] C. Sinoquet. Probabilistic graphical modeling in systems biology: a framework for integrative approaches. In Systems Biology : integrative biology and simulation tools. 01-01-2013
      https://hal.science/hal-01168778

Autres publications (AUTRES)

    • [55] C. Sinoquet. Approches par optimisation combinatoire et par apprentissage statistique en bioinformatique. Applications pour la fouille et la modélisation de données complexes en génomique et en génétique.
      https://hal.science/hal-01168797
    • [56] V. Perduca, R. Mourad, C. Sinoquet, G. Nuel. Waffect: a method to simulate case-control samples in genome-wide association studies.
      https://hal.science/hal-00915533
    • [58] T. Morisseau, R. Mourad, C. Dina, P. Leray, C. Sinoquet. GWAS-AS: assistance for a thorough evaluation of advanced algorithms dedicated to genome-wide association studies.
      https://hal.science/hal-00915535
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