SISTM

Statistics in systems biology and translationnal medicine

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Sandrine Darmigny

Mélanie Prague
SISTM Director

Mélanie Prague is currently Inria Research Faculty (Chargé de Recherche) at Inserm, where she is leading the SISTM team from both Inria Centre at the University of Bordeaux and Inserm Bordeaux Population Health research center U1219. She obtained her H.D.R. (Habilitation à Diriger des Recherches) in 2024.

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Boris Hejblum
SISTM Deputy Director

Boris Hejblum is currently Research Faculty (Chargé de Recherche) at Inserm, where he is co-leading the SISTM team from both Inria Centre at the University of Bordeaux and Inserm Bordeaux Population Health research center U1219. He obtained his H.D.R. (Habilitation à Diriger des Recherches) in 2024.

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Informations Downloads Publications

Objectives

The team is devoted to the development of statistical methods for the integrative analysis of data in medicine and biology. Thanks to the technological improvements, clinical and biological research is generating massive amounts of data. Of importance are the « omics » data such as genomics (gene expression) and proteomics but also other types of data, for which modern technologies have strongly increased the amount of information (e.g. medical imaging, cell counts).
This SISTM team is labelled by both Inserm and Inria.

The applications are carried out in collaboration with the Vaccine Research Institute (VRI), other teams from the center as well as the Methodological Support Unit for Clinical and Epidemiological Research (USMR) of the Bordeaux University Hospital.

The two main objectives of the SISTM team are:

To accelerate the development of vaccines by analyzing all the information available in early clinical trials and optimizing new trials.
To develop new data science approaches to analyze and model high dimensional data in small sample size studies.

Research area

The team is organized around three axes sharing a common objective. It is embarked in a double challenge of developing methods to deal with high dimensional data with low sample size and a main application for accelerating vaccine development.

Hence in Axis 1, the relevant information is extracted from big data. This information is used to estimate mechanistic model parameters in Axis 2. Mechanistic models can then be used for simulating the optimal vaccine strategies to be evaluated in the next clinical trials.

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Research area

Axis 1 High Dimensional Statistical Learning

To unlock the analysis of high-dimensional longitudinal data by developing suitable statistical approaches, in particular for applications to high-throughput data (e.g. microbiome, transcriptome, cytomics) generated in vaccine trials.
To leverage prior biological knowledge and formally incorporate it into statistical models to tackle the small n large p setting, one of the characteristics of early phase vaccine trials.
To advance adaptive clustering methods of high-dimensional data in both supervised and unsupervised settings, especially to infer the proportions of cellular population from gene expression measurements and also to identify gene whose expression is key in segmenting transcriptomic measurements across vaccine arms or disease severity for instance.
To perform feature selection and dimension reduction of high-dimensional molecular and cellular data, as a first step to feed such information into mechanistic models.

Axis 2 Mechanistic learning

To infer ordinary differential equations (ODE) systems parameters by using high dimensional data. We account for multiple markers measured with errors, sparsity in data sampling and inter-individual variability using non-linear mixed effects models.
To compare and implement control strategies through various approaches belonging to statistical control, stochastic control and reinforcement learning

Axis 3 Translational vaccinology

To accelerate vaccine development by in silico trials
To accelerate vaccine development by new adaptive designs
To accelerate vaccine development by in depth analysis of data generated in early clinical trials

Les applications sont réalisées en collaboration avec the Vaccine Research Institute (VRI), d’autres équipes du centre ainsi que l’Unité de Soutien Méthodologique à la Recherche Clinique et Epidémiologique (USMR) du CHU de Bordeaux

Downloads

marqLevAlg

For function optimization

VSURF

Variable Selection Using Random Forests

TcGSA

Gene set analysis of longitudinal gene expression data sets

TcGSA

Development version

R2GUESS

Graphical processing Unit Evolutionary Stochastic Search

sgPLS

Sparse Group Partial Least Square package

NPflow

Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

NPflow

Development version

ClogitLasso

For Lasso Estimation of Conditional Logistic
Regression Models for matched case-control and case-crossover studies

Reference Curves

For CD4+ T Cell Count Response to Combination of Antiretroviral Treatment in HIV-1 Infected Naïve Patients

2023 Key publications

Agniel D, Hejblum BP, Thiebaut R, Parast L. Doubly robust evaluation of high-dimensional surrogate markers. Biostatistics. 2023;24(4):985-99. https://doi.org/10.1093/biostatistics/kxac020

Alexandre M, Prague M, McLean C, Bockstal V, Douoguih M, Thiebaut R, Consortia EaE. Prediction of long-term humoral response induced by the two-dose heterologous Ad26.ZEBOV, MVA-BN-Filo vaccine against Ebola. NPJ Vaccines. 2023;8(1):174. https://doi.org/10.1038/s41541-023-00767-y

Blengio F, Hocini H, Richert L, Lefebvre C, Durand M, Hejblum B, Tisserand P, McLean C, Luhn K, Thiebaut R, Levy Y. Identification of early gene expression profiles associated with long-lasting antibody responses to the Ebola vaccine Ad26.ZEBOV/MVA-BN-Filo. Cell Rep. 2023;42(9):113101. https://doi.org/10.1016/j.celrep.2023.113101

Choi EM-L, Lacarra B, Afolabi MO, Ale BM, Baiden F, Betard C, Foster J, Hamze B, Schwimmer C, Manno D, D’Ortenzio E, Ishola D, Keita CM, Keshinro B, Njie Y, van Dijck W, Gaddah A, Anumendem D, Lowe B, Vatrinet R, Lawal BJ, Otieno GT, Samai M, Deen GF, Swaray IB, Kamara AB, Kamara MM, Diagne MA, Kowuor D, McLean C, Leigh B, Beavogui AH, Leyssen M, Luhn K, Robinson C, Douoguih M, Greenwood B, Thiebaut R, Watson-Jones D. Safety and immunogenicity of the two-dose heterologous Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen in infants: a phase 2, randomised, double-blind, active-controlled trial in Guinea and Sierra Leone. Lancet Glob Health. 2023;11(11):e1743-e52. https://doi.org/10.1016/S2214-109X(23)00410-2

Clairon Q, Pasin C, Balelli I, Thiebaut R, Prague M. Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control approach. Computation Stat. 2023. https://doi.org/10.1007/s00180-023-01420-x

Clairon Q, Prague M, Planas D, Bruel T, Hocqueloux L, Prazuck T, Schwartz O, Thiebaut R, Guedj J. Modeling the kinetics of the neutralizing antibody response against SARS-CoV-2 variants after several administrations of Bnt162b2. PLoS Comput Biol. 2023;19(8):e1011282. https://doi.org/10.1371/journal.pcbi.1011282

Collin A, Hejblum BP, Vignals C, Lehot L, Thiebaut R, Moireau P, Prague M. Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions. Int J Biostat. 2023. https://doi.org/10.1515/ijb-2022-0087

Devaux A, Helmer C, Genuer R, Proust-Lima C. Random survival forests with multivariate longitudinal endogenous covariates. Stat Methods Med Res. 2023;32(12):2331-46. https://doi.org/10.1177/09622802231206477

Dong L, Moodie EEM, Villain L, Thiebaut R. Evaluating the Use of Generalized Dynamic Weighted Ordinary Least Squares for Individualized Hiv Treatment Strategies. Ann Appl Stat. 2023;17(3):2432-51. https://doi.org/10.1214/22-aoas1726

Freulon P, Bigot J, Hejblum BP. Cytopt: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry Data. Ann Appl Stat. 2023;17(2):1086-104. https://doi.org/10.1214/22-Aoas1660

View all of the team’s publications in Oskar

Last News

Posted on : 29 Apr 2024

Launch of CAP Digital Health within the framework of the call for expressions of interest "Skills and Future Professions"

Events

Launch of CAP Digital Healthwithin the framework of the call for expressions of interest“Skills and Future Professions” Three winning projects from the university within the call for expressions of interest “Skills and Future Professions” were launched on Tuesday, April 9th. One of these projects is CAP Digital Health led by Rodolphe THIEBAUT (Director of BPH); […]

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Posted on : 07 Feb 2024

Covid-19 : Etude de l'efficacité des mesures instaurées contre le virus en France

Press

Covid-19 : Etude de l’efficacité des mesures instaurées contre le virus en France Des chercheurs de l’université et du CHU de Bordeaux, de l’Inserm et d’Inria publient des estimations sur l’efficacité des mesures restrictives (confinement, couvre-feu…) et de la politique vaccinale à partir d’une modélisation mathématique des données françaises. Ces résultats sont publiés dans la […]

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Posted on : 20 Jun 2023

The BPH SISTM team is accredited by both Inria & Inserm

Team

The SISTM team – © Inria / Photo F. Stucin The BPH SISTM team is accredited by both Inria & Inserm The two main objectives of the SISTM team (Statistics in Systems biology and Translationnal Medicine), led by Pr Rodolphe Thiébaut, are: i) to accelerate the development of vaccines by analyzing all the information available […]

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Posted on : 02 Jan 2023

More than 3 M€ for our project UB 2030 CAP Digital Health labellized by the call AMI CMA "Skills and future professions" of the 2030 France national plan

Awards

This new project coordinated by Rodolphe Thiébaut leader of the BPH’s SISTM team, is part of the 70 awarded of the second call for expression of interest AMI CMA 2022. The ambition is to offer innovative training schemes in the field of digital health. The project UB 2030 CAP Digital Health brings together a large […]

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