Publications HAL des membres

Christine Sinoquet

      2020

    • [1] 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
      Hugo Boisaubert, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-02455132

      2018

    • [3] Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.
      Christine Sinoquet, Kamel Mekhnacha.
      https://hal.archives-ouvertes.fr/hal-01986660
    • [4] Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data.
      Christine Sinoquet, Kamel Mekhnacha.
      https://hal.archives-ouvertes.fr/hal-01986653
    • [7] Enhanced ensemble approach to learn Markov blankets for feature subset selection in high-dimensional settings. Illustration with an application to mine genetic data.
      Clément Niel, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-01986670
    • [10] A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies
      Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-01984726
    • [11] Optimisation par colonie de fourmis pour la sélection de variables par construction stochastique de couverture de Markov - Application pour la médecine de précision.
      Clément Niel, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-01986671
    • [14] Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-wide Association Studies. Which clustering should be chosen?
      Duc-Thanh Phan, Philippe Leray, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-01204956

      2014

    • [16] Modeling genetical data with forests of latent trees for applications in association genetics at a large scale. Which clustering method should be chosen?
      Duc-Thanh Phan, Philippe Leray, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-01084907
    • [20] Modeling linkage disequilibrium and performing association studies through probabilistic graphical models: a visiting tour of recent advances.
      Christine Sinoquet, Raphaël Mourad.
      https://hal.archives-ouvertes.fr/hal-01168755
    • [21] 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
      Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-01168797
    • [22] Proceedings of the 5th International Conference on Bioinformatics Models, Methods and Algorithms (Bioinformatics2014)
      Oscar Pastor, Christine Sinoquet, Guy Plantier, Tanja Schultz, Ana Fred, Hugo Gamboa.
      https://hal.archives-ouvertes.fr/hal-01169027
    • [23] Biomedical Engineering Systems and Technologies, 7th International Joint Conference, BIOSTEC2014, Extended Selected Papers
      Alberto Cliquet Jr, Mário Forjaz Secca, Jan Schier, Oscar Pastor, Christine Sinoquet, Harald Loose, Marta Bienkiewicz, Christine Verdier, Guy Plantier, Tania Schultz, Ana Fred, Hugo Gamboa.
      https://hal.archives-ouvertes.fr/hal-01169026
    • [37] A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies.
      Raphaël Mourad, Christine Sinoquet, Philippe Leray.
      https://hal.archives-ouvertes.fr/hal-00567988
    • [39] GWAS-AS: assistance for a thorough evaluation of advanced algorithms dedicated to genome-wide association studies
      Thomas Morisseau, Raphaël Mourad, Christian Dina, Philippe Leray, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-00915535
    • [43] Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiques
      Raphaël Mourad, Christine Sinoquet, Philippe Leray.
      https://hal.archives-ouvertes.fr/hal-00484705
    • [50] Temporal constraints of a gene regulatory network: Refining a qualitative simulation.
      Jamil Ahmad, Jérémie Bourdon, Damien Eveillard, Jonathan Fromentin, Olivier Roux, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-00415921
    • [51] Temporal constraints of a gene regulatory network: refining a qualitative simulation
      Jamil Ahmad, Jérémie Bourdon, Damien Eveillard, Jonathan Fromentin, Olivier Roux, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-00423353
    • [52] Qualitative modelling and analysis of gene regulatory networks: application to the adaptation of Escherichia coli bacterium to carbon availability
      Jamil Ahmad, Jérémie Bourdon, Damien Eveillard, Jonathan Fromentin, Olivier Roux, Christine Sinoquet.
      https://hal.archives-ouvertes.fr/hal-00359530

      2007

    • [57] A large-scale computational analysis for significance assessment of frequencies relative to potentially strong sigma 70 promoters - comparison between 32 bacterial genomes
      Christine Sinoquet, Sylvain Demey, Frédérique Braun.
      https://hal.archives-ouvertes.fr/hal-00153303v3
    • [58] Genome-comparative computational approach for investigating prokaryotic ORF expression potentialities, in relation with potentially high transcription
      Christine Sinoquet, Sylvain Demey, Frédérique Braun.
      https://hal.archives-ouvertes.fr/hal-00163675
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Groupement de Recherche en Intégration de données Omics à Très grande Echelle