A Novel Approach for Structured Consensus Motif Inference Under Specificity and Quorum Constraints. - Christine Sinoquet (APBC, 2006)

Maximal sub-triangulation in pre-processing phylogenetic data. - Anne Berry; Alain Sigayret; Christine Sinoquet (Soft Comput., 2006)

Extending the Hardness of RNA Secondary Structure Comparison. - Guillaume Blin; Guillaume Fertin; Irena Rusu; Christine Sinoquet (ESCAPE, 2007)

Temporal constraints of a gene regulatory network: Refining a qualitative simulation. - Jamil Ahmad; Jérémie Bourdon; Damien Eveillard; Jonathan Fromentin; Olivier F. Roux; Christine Sinoquet (Biosyst., 2009)

Iterative Two-Pass Algorithm for Missing Data Imputation in SNP Arrays. - Christine Sinoquet (J. Bioinform. Comput. Biol., 2009)

Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies. - Raphaël Mourad; Christine Sinoquet; Philippe Leray 001 (COMPSTAT, 2010)

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 001 (BMC Bioinform., 2011)

Forests of Latent Tree Models for the Detection of Genetic Associations. - Christine Sinoquet; Raphaël Mourad; Philippe Leray 001 (BIOINFORMATICS, 2012)

Forests of Latent Tree Models to Decipher Genotype-Phenotype Associations. - Christine Sinoquet; Raphaël Mourad; Philippe Leray 001 (BIOSTEC (Selected Papers), 2012)

Probabilistic graphical models for genetic association studies. - Raphaël Mourad; Christine Sinoquet; Philippe Leray 001 (Briefings Bioinform., 2012)

A Survey on Latent Tree Models and Applications. - Raphaël Mourad; Christine Sinoquet; Nevin Lianwen Zhang; Tengfei Liu; Philippe Leray 001 (J. Artif. Intell. Res., 2013)

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 001; Christine Sinoquet (BIOINFORMATICS, 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 001; Christine Sinoquet (BIOSTEC (Selected Papers), 2015)

Random Forest Framework Customized to Handle Highly Correlated Variables: An Extensive Experimental Study Applied to Feature Selection in Genetic Data. - Christine Sinoquet; Kamel Mekhnacha (DSAA, 2018)

Combining latent tree modeling with a random forest-based approach, for genetic association studies. - Christine Sinoquet; Kamel Mekhnacha (ESANN, 2018)

Enhancement of a stochastic Markov-blanket framework with ant colony optimization, to uncover epistasis in genetic association studies. - Christine Sinoquet; Clément Niel (ESANN, 2018)

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 (IDA, 2018)

Ant Colony Optimization for Markov Blanket-Based Feature Selection. Application for Precision Medicine. - Christine Sinoquet; Clément Niel (LOD, 2018)

SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS. - Clément Niel; Christine Sinoquet; Christian Dina; Ghislain Rocheleau (Bioinform., 2018)

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 (BMC Bioinform., 2018)

Detection of Gene-gene Interactions: Methodological Comparison on Real-World Data and Insights on Synergy between Methods. - Hugo Boisaubert; Christine Sinoquet (BIOINFORMATICS, 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 Protocols. - Hugo Boisaubert; Christine Sinoquet (BIOSTEC (Selected Papers), 2019)

Prediction and Inference in a Partially Hidden Markov-switching Framework with Autoregression. Application to Machinery Health Diagnosis. - Fatoumata Dama; Christine Sinoquet (ICTAI, 2021)

Partially Hidden Markov Chain Linear Autoregressive model: inference and forecasting. - Fatoumata Dama; Christine Sinoquet (CoRR, 2021)

Analysis and modeling to forecast in time series: a systematic review. - Fatoumata Dama; Christine Sinoquet (CoRR, 2021)

Simulation of the Evolution of a Virtual Patient's Physiological Status in the Operating Room: Application to Computer-assisted Anaesthesia Training. - Hugo Boisaubert; Lucas Vincent; Corinne Lejus-Bourdeau; Christine Sinoquet (HEALTHINF, 2022)

Simulating the Vital Signs of a Digital Patient Undergoing Surgery, for the Purpose of Training Anaesthetists. - Hugo Boisaubert; Lucas Vincent; Corinne Lejus-Bourdeau; Christine Sinoquet (BIOSTEC (Selected Papers), 2022)

A Framework for Context-Sensitive Prediction in Time Series - Feasibility Study for Data-Driven Simulation in Medicine. - Fatoumata Dama; Christine Sinoquet; Corinne Lejus-Bourdeau (DSAA, 2023)

A hidden Markov model with Hawkes process-derived contextual variables to improve time series prediction. Case study in medical simulation. - Fatoumata Dama; Christine Sinoquet; Corinne Lejus-Bourdeau (ESANN, 2023)

Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting - application to machine health prognostics. - Fatoumata Dama; Christine Sinoquet (Mach. Learn., 2023)

LSTM encoder-decoder model for contextualized time series forecasting applied to the simulation of a digital patient's physiological variables. - Julien Paris; Christine Sinoquet; Fadoua Taia-Alaoui; Corinne Lejus-Bourdeau (ESANN, 2024)

Deep joint modelling of mixed asynchronous streams - Proof of concept for data-driven simulation of a digital patient under anaesthesia. - Julien Paris; Christine Sinoquet; Fadoua Taia-Alaoui; Corinne Lejus-Bourdeau (KES, 2024)

Expertise versus Data: Comparison of Expertise Based Process Models to Process-Mining Models of Surgery Under General Anesthesia. - Hugo Boisaubert; Chloé Grivaud; Antoine Bouchet; Corinne Lejus-Bourdeau; Christine Sinoquet (BIOSTEC (2): HEALTHINF, 2025)

Two-in-One Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient Under Anesthesia. - Quentin Victor; Ianis Clavier; Hugo Boisaubert; Fabien Picarougne; Corinne Lejus-Bourdeau; Christine Sinoquet (IDA, 2025)