Publications

Preprint

  • 2025 PDF CODE
    Causal inference of post-transcriptional regulation timelines from long-read sequencing in Arabidopsis thaliana
    Martos R., Ambroise C., Rigaill G. — arXiv preprint arXiv:2510.12504 (2025).
  • 2025 PDF CODE
    jmstate, a Flexible Python Package for Multi-State Joint Modeling
    Laplante F., Ambroise C. — arXiv preprint arXiv:2510.07128 (2025).
  • 2024 PDF CODE
    Mixture of multilayer stochastic block models for multiview clustering
    Santiago K., Szafranski M., Ambroise C. — submitted to JMLR (2024).
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Books

  • 2004
    Analyzing microarray gene expression data
    McLachlan G., Do K., Ambroise C. — Wiley (2004).
    Image for Analyzing microarray gene expression data
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Journal articles

  • 2024 PDF
    Bioinformatics manual for population epigenomics combining whole-genome and target genome sequencing
    Rogier O., Kupin I., Sow M., Boury C., Duplan A., Garnier A., Civan P., Daron J., Delaunay A., Duvaux L., others (2024).
  • 2024 PDF
    A strategy for studying epigenetic diversity in natural populations: proof of concept in poplar and oak
    Lesur I., Rogier O., Sow M., Boury C., Duplan A., Garnier A., Senhaji-Rachik A., Civan P., Daron J., Delaunay A., others — Journal of Experimental Botany (2024).
  • 2024 PDF CODE
    Spectral Bridges
    Ambroise C. — Computo (2024).
  • 2023 PDF CODE
    Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations
    Laso-Jadart R., O’Malley M., Sykulski A., Ambroise C., Madoui M. — BMC Ecology and Evolution (2023).
  • 2023 PDF CODE
    Inference of Multiscale Gaussian Graphical Model
    Sanou D., Ambroise C., Robin G. — Computo (2023).
  • 2022 PDF
    A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping
    Courbariaux M., De Santiago K., Dalmasso C., Danjou F., Bekadar S., Corvol J., Martinez M., Szafranski M., Ambroise C. — Frontiers in Genetics (2022).
  • 2022 PDF CODE
    Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process
    Bichat A., Ambroise C., Mariadassou M. — Computational Statistics (2022).
  • 2021 PDF CODE
    Accounting for missing actors in interaction network inference from abundance data
    Momal R., Robin S., Ambroise C. — Journal of the Royal Statistical Society Series C: Applied Statistics (2021).
  • 2021 PDF CODE
    AI-based mobile application to fight antibiotic resistance
    Pascucci M., Royer G., Adamek J., Asmar M., Aristizabal D., Blanche L., Bezzarga A., Boniface-Chang G., Brunner A., Curel C., Ambroise C., others — Nature Communications (2021).
  • 2020 PDF
    Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866)
    Laso-Jadart R., Sugier K., Petit E., Labadie K., Peterlongo P., Ambroise C., Wincker P., Jamet J., Madoui M. — Ecology and Evolution (2020).
  • 2020 PDF CODE
    Tree-based inference of species interaction networks from abundance data
    Momal R., Robin S., Ambroise C. — Method in ecology and evolution (2020).
  • 2020 PDF CODE
    PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph
    Gautreau G., Bazin A., Gachet M., Planel R., Burlot L., Dubois M., Perrin A., Médigue C., Calteau A., Cruveiller S., Ambroise C., others — PLoS computational biology (2020).
  • 2020 PDF CODE
    metaVaR: introducing metavariant species models for reference-free metagenomic-based population genomics
    Laso-Jadart R., Ambroise C., Peterlongo P., Madoui M. — Plos one (2020).
  • 2020 PDF CODE
    Fast computation of genome-metagenome interaction effects
    Guinot F., Szafranski M., Chiquet J., Zancarini A., Le Signor C., Mougel C., Ambroise C. — Algorithms for Molecular Biology (2020).
  • 2020 PDF
    Incorporating phylogenetic information in microbiome differential abundance studies has no effect on detection power and FDR control
    Bichat A., Plassais J., Ambroise C., Mariadassou M. — Frontiers in microbiology (2020).
  • 2020 PDF
    Exploring the link between additive heritability and prediction accuracy from a ridge regression perspective
    Frouin A., Dandine-Roulland C., Pierre-Jean M., Deleuze J., Ambroise C., Le Floch E. — Frontiers in Genetics (2020).
  • 2019 PDF
    A generalized statistical framework to assess mixing ability from incomplete mixing designs using binary or higher order variety mixtures and application to wheat
    Forst E., Enjalbert J., Allard V., Ambroise C., Krissaane I., Mary-Huard T., Robin S., Goldringer I. — Field Crops Research (2019).
  • 2019 PDF CODE
    Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics
    Ambroise C., Dehman A., Neuvial P., Rigaill G., Vialaneix N. — Algorithms for Molecular Biology (2019).
  • 2019
    Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples
    Palomares M., Dalmasso C., Bonnet E., Derbois C., Brohard-Julien S., Ambroise C., Battail C., Deleuze J., Olaso R. — Scientific Reports (2019).
  • 2018
    Incomplete graphical model inference via latent tree aggregation
    Robin G., Ambroise C., Robin S. — Statistical Modeling (2018).
  • 2018
    Epigenetics in forest trees: state of the art and potential implications for breeding and management in a context of climate change
    Sow M., Allona I., Ambroise C., Conde D., Fichot R., Gribkova S., Jorge V., Le-Provost G., Pâques L., Plomion C., others — Advances in botanical research (2018).
  • 2018
    Learning the optimal scale for GWAS through hierarchical SNP aggregation
    Guinot F., Szafranski M., Ambroise C., Samson F. — BMC Bioinformatics (2018).
  • 2017
    Eigen-Epistasis for detecting gene-gene interactions
    Stanislas V., Dalmasso C., Ambroise C. — BMC bioinformatics (2017).
  • 2016
    Beyond Support in Two-Stage Variable Selection
    Becu J., Grandvalet Y., Ambroise C., Dalmasso C. — Statistics and computing (2016).
  • 2015
    Performance of a blockwise approach in variable selection using linkage disequilibrium information
    Dehman A., Ambroise C., Neuvial P. — BMC bioinformatics (2015).
  • 2014
    Model selection in overlapping stochastic block models
    Latouche P., Birmele E., Ambroise C. — Electron. J. Statist. (2014).
  • 2012
    SHIPS: spectral hierarchical clustering for the inference of population structure in genetic studies
    Bouaziz M., Paccard C., Guedj M., Ambroise C. — PloS one (2012).
  • 2012 PDF
    New consistent and asymptotically normal parameter estimates for random graph mixture models
    Ambroise C., Matias C. — Journal of the Royal Statistical Society: Series B (2012).
  • 2012
    Variational Bayesian Inference and Complexity Control for Stochastic Block Models
    Latouche P., Birmele E., Ambroise C. — Statistical Modelling (2012).
  • 2011 PDF
    Accounting for Population Stratification in Practice: a Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies
    Bouaziz M., Ambroise C., Guedj M. — PLOS one (2011).
  • 2011
    Inferring Multiple Graphical Structures
    Chiquet J., Grandvalet Y., Ambroise C. — Statistics and Computing (2011).
  • 2011
    Defining a robust biological prior from Pathway Analysis to drive Network Inference.
    Jeanmougin M., Guedj M., Ambroise C. — J-SFdS (2011).
  • 2011
    Overlapping Stochastic Block Models with Application to the French Political Blogosphere
    Latouche P., Birmele E., Ambroise C. — Annals of Applied Statistics (2011).
  • 2010
    Strategies for Online Inference of Network Mixture
    Zanghi H., Picard F., Miele V., Ambroise C. — Annals of Applied Statistics (2010).
  • 2010
    Clustering based on random graph model embedding vertex features
    Zanghi H., Volant S., Ambroise C. — Pattern Recognition Letters (2010).
  • 2010
    Inferring Multiple Graphical Structures
    Chiquet J., Grandvalet Y., Ambroise C. — Statistics and Computing (2010).
  • 2010
    Weighted-Lasso for Structured Network Inference from Time Course Data
    Charbonnier C., Chiquet J., Ambroise C. — Statistical Applications in Genetics and Molecular Biology (2010).
  • 2009
    Inferring sparse Gaussian graphical models with latent structure
    Ambroise C., Chiquet J., Matias C. — Electron. J. Statist. (2009).
  • 2008
    Fast Online Graph Clustering via Erdos Renyi Mixture
    Zanghi H., Ambroise C., Miele V. — Pattern Recognition (2008).
  • 2008
    Identification of functional modules based on transcriptional regulation structure
    Birmele E., Elati M., Rouveirol C., Ambroise C. — BMC Proceedings (2008).
  • 2008
    SIMoNe : Statistical Inference for MOdular NEtworks
    Chiquet J., Smith A., Grasseau G., Matias C., Ambroise C. — Bioinformatics (2008).
  • 2007
    An online Classification EM algorithm based on the mixture model
    Same A., Ambroise C., Govaert G. — Statistics and Computing (2007).
  • 2007
    Parsimonious additive models
    Avalos M., Grandvalet Y., Ambroise C. — CSDA (2007).
  • 2006
    Selection bias in working with the top genes in supervised classification of tissue samples
    Zhu X., Ambroise C., McLachlan G. — Statistical Methodology (2006).
  • 2006
    A classification EM algorithm for binned data
    Same A., Ambroise C., Govaert G. — Computational Statistics and Data Analysis (2006).
  • 2006
    Feature Selection in Robust Clustering based on Laplace Mixture
    Cord A., Ambroise C., Cocquerez J. — Pattern Recognition Letters (2006).
  • 2005
    Discrimination par modeles additifs parcimonieux
    Avalos M., Grandvalet Y., Ambroise C. — Revue d'Intelligence Artificielle (2005).
  • 2002 PDF
    Selection Bias in Gene Extraction in Tumour Classification on Basis of Microarray Gene Expression Data
    Ambroise C., McLachlan G. — PNAS (2002).
  • 2001
    Prediction of ozone peaks by mixture model
    Ambroise C., Grandvalet Y. — Ecological Modeling (2001).
  • 1998 PDF CODE
    Convergence Proof of an EM-type Algorithm for Spatial Clustering
    Ambroise C., Govaert G. — Pattern Recognition Letters (1998).
  • 1998 PDF
    Hierarchical clustering of self organizing map for cloud classification
    Ambroise C., Seze G., Badran S., Thiria S. — Neurocomputing (1998).
  • 1996 PDF
    Constrained Clustering and Kohonen Self-Organizing Maps
    Ambroise C., Govaert G. — Journal of Classification (1996).
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Book chapters

  • 2022
    Compression structurée de l'information génétique et étude d'association pangénomique par modèles additifs
    GUINOT F., SZAFRANSKI M., AMBROISE C. — Intégration de données biologiques: Approches informatiques et statistiques (2022).
  • 2019
    Applications in Genomics
    Robin S., Ambroise C. — Handbook of Mixture Analysis (2019).
  • 2018
    Chapter Twelve - Epigenetics in Forest Trees: State of the Art and Potential Implications for Breeding and Management in a Context of Climate Change
    Sow M., Allona I., Ambroise C., Conde D., Fichot R., Gribkova S., Jorge V., Le-Provost G., Pâques L., Plomion C., Salse J., Sanchez-Rodriguez L., Segura V., Tost J., Maury S. — Plant Epigenetics Coming of Age for Breeding Applications (2018).
  • 2015
    Overlapping clustering methods for Networks
    Latouche P., Birmele E., Ambroise C. — Handbook of Mixed Membership Models and Their Applications (2015).
  • 2009
    Spatial Data Clustering
    Ambroise C., Dang M. — Data Analysis (2009).
  • 2009
    Bayesian methods for graph clustering
    Latouche P., Birmele E., Ambroise C. — Advances in Data Analysis, Data Handling and Business Intelligence (2009).
  • 2005
    Use of microarray data via model-based classification in the study and prediction of survival from lung cancer
    Jones L., Ng S., Ambroise C., Monico K., McLachlan G. — The fourth international conference for the Critical Assessment of Microarray Data Analysis (CAMDA 2003) (2005).
  • 2003
    Classification automatique de donnees spatiales
    Ambroise C., Dang M. — Analyse de donnees (2003).
  • 2001
    Clustering and models
    Ambroise C., Govaert G. — Classification, Automation and New Media. Proceedings of the 24th Annual Conference of the Gesellshaft fuer Klassication (2001).
  • 1997 PDF CODE
    Clustering of spatial data by the EM algorithm
    Ambroise C., Dang M., Govaert G. — Geostatistics for Environmnental Applications (1997).
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Conference proceedings

  • 2023
    Mixture of stochastic block models for multiview clustering
    De Santiago K., Szafranski M., Ambroise C. — ESANN 2023-European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2023).
  • 2019
    PanGBank: depicting microbial species diversity via PPanGGOLiN
    Gautreau G., Bazin A., Planel R., Gachet M., Dubois M., Burlot L., Perrin A., Touchon M., Rocha E., Ambroise C., others — JOBIM 2019 Journées Ouvertes Biologie, Informatique et Mathématiques (2019).
  • 2018
    Une approche hiérarchique de la recherche d'interactions entre données omiques
    Guinot F., Szafranski M., Ambroise C. — Journées de Statistique de la SFdS proceedings (2018).
  • 2018
    Quantifier l'héritabilité génomique via une mesure de prédiction
    Frouin A., Lefloch E., Ambroise C. — Journées de Statistique de la SFdS proceedings (2018).
  • 2018
    Sous typage de maladie avec étude d'association génétique intégrée
    Courbariaux M., Szafranski M., Dalmasso C., Ambroise C. — Journées de Statistique de la SFdS proceedings (2018).
  • 2018
    Clarifying the role of DNA methylation in tree phenotypic plasticity
    Sow M., Le Gac A., Placette C., Delaunay A., Le Jan I., Fichot R., Maury S., Mirouze M., Lanciano S., Tost J., others — 43. FEBS Congress, Biochemistry Forever (2018).
  • 2018
    Estimation of mixing ability for variety mixtures: Statistical models and experimental results
    Forst E., Enjalbert J., Allard V., Ambroise C., Krissaane I., Marry-Huard T., Robin S., Goldringer I. — SYMPOSIUM ON BREEDING FOR DIVERSIFICATION (2018).
  • 2018
    Latent Tree based Inference of Ecological Network using the Poisson Log-Normal Model
    Momal R., Robin S., Ambroise C. — JOBIM 2018 (2018).
  • 2018
    PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome Graph
    Gautreau G., and Ambroise C., Matias C., Planel R., Sabatet V., Perrin A., Touchon M., Rocha E., Medigue C., Cruveiller S., Vallenet D. — JOBIM 2018 (2018).
  • 2015
    {A greedy great approach to learn with complementary structured datasets}
    Ambroise C., Chiquet J., Szafranski M. — {Greed Is Great ICML Workshop} (2015).
  • 2015
    Significance testing for variable selection in high-dimension
    Bécu J., Ambroise C., Grandvalet Y., Dalmasso C. — Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015 IEEE Conference on (2015).
  • 2013
    Incorporating linkage disequilibrium blocks in Genome-Wide Association Studies
    Dehman A., Ambroise C., Neuvial P. — JOBIM proceeding 2013 (2013).
  • 2012
    Improving gene signatures by the identification of differentially expressed modules in molecular networks : a local-score approach.
    Jeanmougin M. — JOBIM (2012).
  • 2010
    Inférence jointe de la structure de modèles graphiques gaussiens
    Grandvalet Y., Chiquet J., Ambroise C. — actes de CAp'10, Clermont-Ferrand (2010).
  • 2010
    Weighted-Lasso for Structured Network Inference for Time-Course data
    Charbonnier C., Chiquet J., Ambroise C. — JOBIM'10, Montpellier (2010).
  • 2010
    Inferring Multiple Graphical Structures
    Chiquet J., Grandvalet Y., Ambroise C. — Workshop MODGRAPHII, JOBIM'10, Montpellier (2010).
  • 2010
    Inferring Multiple Regulation Networks
    Grandvalet Y., Chiquet J., Ambroise C. — Proceedings of the MLCB NIPS'10 Workshop, Vancouver (2010).
  • 2009
    Uncovering overlapping clusters in biological networks
    Latouche P., Birmelé E., Ambroise C. — Journées ouvertes en biologie, informatique et mathématiques (Jobim). Nantes (2009).
  • 2009
    SIMoNe : Statistical Inference of Modular Network
    Chiquet J., Charbonnier C., Ambroise C. — Workshop MODGRAPH, JOBIM'09, Nantes (2009).
  • 2006
    Model based hierarchical co-clustering
    Ambroise C., Govaert G. — COMPSTAT 2006 (2006).
  • 2006
    Driving hierarchy construction via supervised learning: Application to Osteo-Articular medical images database
    Yousfi K., Ambroise C., Cocquerez J., Chevelu J. — ICIP 06 : IEEE International Conference on Image Processing (2006).
  • 2006
    Supervised learning for guiding hierarchy construction: Application to Osteo-Articular medical images database
    Yousfi K., Ambroise C., Cocquerez J., Chevelu J. — ICPR 06 : IEEE International Conference on Pattern Recognition (2006).
  • 2005
    A classifer solution for several classes simultaneous occurrence : application to vehicle failure isolation
    Charkaoui N., Dubuisson B., Ambroise C., Millemann S. — Proceedings PRIP05 Eighth International Conference on Pattern Recognition and Information Processing (2005).
  • 2005
    Pattern recognition method for offboard automotive vehicle failure isolation
    Charkaoui N., Dubuisson B., Ambroise C., Boatas A. — In Proceedings of 16th IFAC World Congress (2005).
  • 2005
    Interpretable Clustering via Model-Based Divisive Hierarchical Classification
    Sujka N., Govaert G., Ambroise C. — 29th Annual GFKL (Gesellschaft für Klassifikation) (2005).
  • 2004
    Decision tree classifer for vehicle failure isolation
    Charkaoui N., Dubuisson B., Ambroise C., Millemann S. — Fifth International Conference on Data Mining, Text Mining and their Business Applications (2004).
  • 2004
    A mixture model approach for on-line clustering
    Same A., Ambroise C., Govaert G. — COMPSTAT 2004 Proceedings (2004).
  • 2004
    Penalized additive logistic regression for cardiovascular risk prediction.
    Avalos M., Grandvalet Y., Ambroise C. — International Conference on Statistics in Health Sciences (2004).
  • 2004
    A mixture model approach for acoustic emission control of pressure equipment
    Hamdan H., Govaert G., Ambroise C., Hervé C. — 5th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques (2004).
  • 2004
    Discriminative Classification vs Modeling Methods in CBIR
    Gosselin P., Najjar M., Cord M., Ambroise C. — Proc. of Conf. on Advanced Concepts for Intelligent Vision Systems, ACIVS'2004 (2004).
  • 2003
    Image Retrieval Using Mixture Models and EM Algorithm
    Najjar M., Ambroise C., Cocquerez J. — 13th Scandinavian Conference, SCIA 2003 (2003).
  • 2003
    A mixture model approach for binned data clustering
    Same A., Ambroise C., Govaert G. — Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS) (2003).
  • 2003
    Regularization Methods for Additive Models
    Avalos M., Grandvalet Y., Ambroise C. — Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science (LNCS) (2003).
  • 2003
    Feature Selection for Semi Supervised Learning Applied to Image Retrieval
    Najjar M., Ambroise C., Cocquerez J. — ICIP03, International Conference on Image Processing (2003).
  • 2003
    Use of microarray data via model-based classification in the study and prediction of survival from lung cancer (Accepted as a finalist paper)
    Jones L., Ng S., Ambroise C., Monico K., Mclachlan G. — Challenge at The fourth international conference for the Critical Assessment of Microarray Data Analysis (2003).
  • 2002
    Semi-supervised marginboost
    d'Alché-Buc F., Grandvalet Y., Ambroise C. — Advances in Neural Information Processing Systems 14 (2002).
  • 2002
    A Mixture Model Approach to Datacube Clustering (Invited)
    Ambroise C., Govaert G. — 26th Annual GFKL (Gesellschaft für Klassifikation) (2002).
  • 2002
    Classification de données discrètisées
    Same A., Govaert G., Ambroise C. — 34ème journées de statistiques (2002).
  • 2001
    Learning from an imprecise teacher: probabilistic and evidential approaches
    Ambroise C., Denoeux T., Govaert G., Smets P. — Proceeding of ASMDA 2001 (2001).
  • 2001
    Intégration de données qualitatives et quantitatives par les modèles de mélange(Invited)
    Ambroise C. — Journée Didactique IS2 sur les Mélanges de Lois de Probabilités (2001).
  • 2001
    Boosting Mixture Models for semi-supervised tasks
    Grandvalet Y., D'alché-Buc F., Ambroise C. — ICANN 2001 (2001).
  • 2001
    Clustering and models
    Ambroise C., Govaert G. — Classification, Automation and New Media. Proceedings of the 24th Annual Conference of the Gesellshaft für Klassification (2001).
  • 2000
    Mixture Models and Clustering (Invited)
    Ambroise C., Govaert G. — 24th Annual GFKL (Gesellschaft für Klassifikation) (2000).
  • 2000
    Clustering by Maximizing a Fuzzy Classification Maximum Likelihood Criterion
    Ambroise C., Govaert G. — Compstat 2000, Prodeedings in Computational Statistics, 14th Symposium held in Utrecht, The Netherlands (2000).
  • 2000
    EM Algorithm for Partially Known Labels
    Ambroise C., Govaert G. — Data Analysis, Classification, and Related Methods, Proceedings of the 7th Conference of the International Federation of Classication Societies (IFCS-2000), University of Namur, Belgium (2000).
  • 1999
    Local learning by sparse radial basis functions
    Granvalet Y., Ambroise C., Canu S. — ICANN99 (1999).
  • 1996 PDF
    Analyzing Dissimilarity Matrices using Kohonen Maps
    Ambroise C., Govaert G. — Proceeding of IFCS96 (1996).
  • 1995
    Self-organization for Gaussian Parsimonious Clustering
    Ambroise C., Govaert G. — Proceeding of ICANN1995 (1995).
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PhD thesis

  • 1996 PDF
    Approche probabiliste en classification automatique et contraintes de voisinage
    Christophe Ambroise —
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