Christine Sinoquet - researchr alias

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Viewing Publication 1 - 27 from 27

2023

A Framework for Context-Sensitive Prediction in Time Series - Feasibility Study for Data-Driven Simulation in MedicineFatoumata Dama, Christine Sinoquet, Corinne Lejus-Bourdeau. dsaa 2023: 1-12 [doi]

Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting - application to machine health prognosticsFatoumata Dama, Christine Sinoquet. ml, 112(1):45-97, January 2023. [doi]

2022

Simulating the Vital Signs of a Digital Patient Undergoing Surgery, for the Purpose of Training AnaesthetistsHugo Boisaubert, Lucas Vincent, Corinne Lejus-Bourdeau, Christine Sinoquet. biostec 2023: 353-376 [doi]

Simulation of the Evolution of a Virtual Patient's Physiological Status in the Operating Room: Application to Computer-assisted Anaesthesia TrainingHugo Boisaubert, Lucas Vincent, Corinne Lejus-Bourdeau, Christine Sinoquet. biostec 2022: 228-239 [doi]

2021

Prediction and Inference in a Partially Hidden Markov-switching Framework with Autoregression. Application to Machinery Health DiagnosisFatoumata Dama, Christine Sinoquet. ictai 2021: 1-9 [doi]

2019

Machine Learning and Combinatorial Optimization to Detect Gene-gene Interactions in Genome-wide Real Data: Looking Through the Prism of Four Methods and Two ProtocolsHugo Boisaubert, Christine Sinoquet. biostec 2020: 139-169 [doi]

Detection of Gene-gene Interactions: Methodological Comparison on Real-World Data and Insights on Synergy between MethodsHugo Boisaubert, Christine Sinoquet. biostec 2019: 30-42 [doi]

2018

SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWASClément Niel, Christine Sinoquet, Christian Dina, Ghislain Rocheleau. bioinformatics, 34(16):2773-2780, 2018. [doi]

Enhancement of a stochastic Markov-blanket framework with ant colony optimization, to uncover epistasis in genetic association studiesChristine Sinoquet, Clément Niel. esann 2018: [doi]

Combining latent tree modeling with a random forest-based approach, for genetic association studiesChristine Sinoquet, Kamel Mekhnacha. esann 2018: [doi]

Random Forests with Latent Variables to Foster Feature Selection in the Context of Highly Correlated Variables. Illustration with a Bioinformatics ApplicationChristine Sinoquet, Kamel Mekhnacha. ida 2018: 290-302 [doi]

Random Forest Framework Customized to Handle Highly Correlated Variables: An Extensive Experimental Study Applied to Feature Selection in Genetic DataChristine Sinoquet, Kamel Mekhnacha. dsaa 2018: 217-226 [doi]

Ant Colony Optimization for Markov Blanket-Based Feature Selection. Application for Precision MedicineChristine Sinoquet, Clément Niel. mod 2019: 217-230 [doi]

A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studiesChristine Sinoquet. bmcbi, 19(1), 2018. [doi]

2015

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. biostec 2015: 169-189 [doi]

Modeling Genetical Data with Forests of Latent Trees for Applications in Association Genetics at a Large Scale - Which Clustering Method should Be Chosen?D.-T. Phan, Philippe Leray, Christine Sinoquet. biostec 2014: 5-16

2013

A Survey on Latent Tree Models and ApplicationsRaphaël Mourad, Christine Sinoquet, N. L. Zhang, T. Liu, Philippe Leray. jair, 47:157-203, 2013. [doi]

2012

Probabilistic graphical models for genetic association studiesRaphaël Mourad, Christine Sinoquet, Philippe Leray. bib, 13(1):20-33, 2012. [doi]

Forests of Latent Tree Models to Decipher Genotype-Phenotype AssociationsChristine Sinoquet, Raphaël Mourad, Philippe Leray. biostec 2013: 113-134 [doi]

Forests of Latent Tree Models for the Detection of Genetic AssociationsChristine Sinoquet, Raphaël Mourad, Philippe Leray. biostec 2012: 5-14

2011

A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studiesRaphael Mourad, Christine Sinoquet, Philippe Leray. bmcbi, 12:16, 2011. [doi]

2010

Learning Hierarchical Bayesian Networks for Genome-Wide Association StudiesRaphaël Mourad, Christine Sinoquet, Philippe Leray. compstat 2010: 549-556 [doi]

2009

Temporal constraints of a gene regulatory network: Refining a qualitative simulationJamil Ahmad, Jérémie Bourdon, Damien Eveillard, Jonathan Fromentin, Olivier Roux, Christine Sinoquet. biosystems, 98(3):149-159, 2009. [doi]

Iterative Two-Pass Algorithm for Missing Data Imputation in SNP ArraysChristine Sinoquet. jbcb, 7(5):833-852, 2009. [doi]

2007

Extending the Hardness of RNA Secondary Structure ComparisonGuillaume Blin, Guillaume Fertin, Irena Rusu, Christine Sinoquet. escape 2007: 140-151 [doi]

2006

Maximal sub-triangulation in pre-processing phylogenetic dataAnne Berry, Alain Sigayret, Christine Sinoquet. soco, 10(5):461-468, 2006. [doi]

A Novel Approach for Structured Consensus Motif Inference Under Specificity and Quorum ConstraintsChristine Sinoquet. apbc 2006: 207-216 [doi]

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