Publications

Santiago K, Szafranski M, Ambroise C (2024). Mixture of multilayer stochastic block models for multiview clustering . submitted to JMLR.

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Sow M, Rogier O, Lesur I, Daviaud C, Mardoc E, Sanou E, Duvaux L, Civan P, Delaunay A, Lesage-Descauses M, others (2024). Epigenetic Variation in Tree Evolution: a case study in black poplar (Populus nigra) . submitted to Plant Cell.

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Sanou D, Ambroise C, Robin G (2023). Inference of Multiscale Gaussian Graphical Model . Computo.

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Laso-Jadart R, O’Malley M, Sykulski A, Ambroise C, Madoui M (2023). Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations . BMC Ecology and Evolution.

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Bichat A, Ambroise C, Mariadassou M (2022). Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process . Computational Statistics.

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Guinot F, Szafranski M, Ambroise C (2022). Compression structur{e}e de l'information g{e}n{e}tique et {e}tude d'association pang{e}nomique par mod{e}les additifs .

Courbariaux M, De Santiago K, Dalmasso C, Danjou F, Bekadar S, Corvol J, Martinez M, Szafranski M, Ambroise C (2022). A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping . Frontiers in Genetics.

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Pascucci M, Royer G, Adamek J, Aristizabal D, Blanche L, Bezzarga A, Boniface-Chang G, Brunner A, Curel C, Dulac-Arnold G, Fakhri R, Malou, N, Nordon C, Runge V, Samson F, Sebastian E, Soukieh D, Vert JP, Ambroise C, Madoui A (2021). AI-based mobile application for antibiotic resistance testing . Nature communications.

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Laso-Jadart R, O’Malley M, Sykulski A, Ambroise C, Madoui M (2021). How marine currents and environment shape plankton genomic differentiation: a mosaic view from Tara oceans metagenomic data . Biorxiv.

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Momal R, Robin S, Ambroise C (2021). Accounting for missing actors in interaction network inference from abundance data . Journal of the Royal Statistical Society Series C: Applied Statistics.

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Momal R, Robin S, Ambroise C (2020). Tree-based Inference of Species Interaction Networks from Abundance Data . Methods in ecology and evolution.

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Gautreau G, Bazin A, Gachet M, Planel R, Burlot L, Dubois M, Perrin A, Medigue C, Calteau A, Cruveiller S, others (2020). PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph . PLoS computational biology.

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Laso-Jadart R, Ambroise C, Peterlongo P, Madoui M (2020). metaVaR: introducing metavariant species models for reference-free metagenomic-based population genomics . Plos one.

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Laso-Jadart R, Sugier K, Petit E, Labadie K, Peterlongo P, Ambroise C, Wincker P, Jamet J, Madoui M (2020). Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866) . Ecology and Evolution.

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Bichat A, Plassais J, Ambroise C, Mariadassou M (2020). Incorporating phylogenetic information in microbiome differential abundance studies has no effect on detection power and FDR control . Frontiers in microbiology.

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Frouin A, Dandine-Roulland C, Pierre-Jean M, Deleuze J, Ambroise C, Floch E (2020). High heritability does not imply accurate prediction under the small additive effects hypothesis . arXiv preprint arXiv:2007.05424.

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Guinot F, Szafranski M, Chiquet J, Zancarini A, Le Signor C, Mougel C, Ambroise C (2020). Fast computation of genome-metagenome interaction effects . Algorithms for Molecular Biology.

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Frouin A, Dandine-Roulland C, Pierre-Jean M, Deleuze J, Ambroise C, Le Floch E (2020). Exploring the link between additive heritability and prediction accuracy from a ridge regression perspective . Frontiers in Genetics.

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Palomares M, Dalmasso C, Bonnet E, Derbois C, Brohard-Julien S, Ambroise C, Battail C, Deleuze J, Olaso R (2019). Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples . Scientific reports.

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Robin G, Ambroise C, Robin S (2019). Incomplete graphical model inference via latent tree aggregation . Statistical Modelling.

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Robin S, Ambroise C (2019). Applications in Genomics .

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Ambroise C, Dehman A, Neuvial P, Rigaill G, Vialaneix N (2019). Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics . Algorithms for Molecular Biology.

Forst E, Enjalbert J, Allard V, Ambroise C, Krissaane I, Mary-Huard T, Robin S, Goldringer I (2019). A generalized statistical framework to assess mixing ability from incomplete mixing designs using binary or higher order variety mixtures and application to wheat . Field Crops Research.

Frouin A, Le Flocch E, Ambroise C (2018). Quantify Genomic Heritability Through a Prediction Measure .

Guinot F, Szafranski M, Ambroise C, Samson F (2018). Learning the optimal scale for GWAS through hierarchical SNP aggregation . BMC bioinformatics.

Forst E, Enjalbert J, Allard V, Ambroise C, Krissaane I, Marry-Huard T, Robin S, Goldringer I (2018). Estimation of mixing ability for variety mixtures: Statistical models and experimental results .

Sow M, Allona I, Ambroise C, Conde D, Fichot R, Gribkova S, Jorge V, Le-Provost G, Paques L, Plomion C, others (2018). Epigenetics in forest trees: state of the art and potential implications for breeding and management in a context of climate change .

Sow M, Le Gac A, Placette C, Delaunay A, Le Jan I, Fichot R, Maury S, Mirouze M, Lanciano S, Tost J, others (2018). Clarifying the role of DNA methylation in tree phenotypic plasticity .

Frouin A, Le Floch E, Ambroise C (2017). Quantify Genomic Heritability Through a Prediction Measure .

Frouin A, Le Floch E, Ambroise C, Deleuze J (2017). Estimating Narrow-Sense Heritability with Ridge Regression .

Stanislas V, Dalmasso C, Ambroise C (2017). Eigen-epistasis for detecting gene-gene interactions . BMC bioinformatics.

Becu J, Grandvalet Y, Ambroise C, Dalmasso C (2017). Beyond support in two-stage variable selection . Statistics and Computing.

Becu J, Ambroise C, Grandvalet Y, Dalmasso C (2015). Significance testing for variable selection in high-dimension .

Dehman A, Ambroise C, Neuvial P (2015). Performance of a blockwise approach in variable selection using linkage disequilibrium information . BMC bioinformatics.

Petit-Teixeira E, Derbois C, Dalmasso C, Olaso R, Ambroise C, Kilani M, Deleuze J, Gabay C, Cornelis F, Finckh A (2015). AB0004 Characterization of Putative Pre-RA Signatures by Transcriptome Analysis .

Chiquet J, Szafranski M, Ambroise C (2015). A greedy great approach to learn with complementary structured datasets .

Latouche P, Birmele E, Ambroise C (2014). Overlapping clustering methods for networks .

Latouche P, Birmele E, Ambroise C, others (2014). Model selection in overlapping stochastic block models . Electronic journal of statistics.

Dehman A, Ambroise C, Neuvial P (2013). Incorporating linkage disequilibrium blocks in Genome-Wide Association Studies . Volume PR{e}SENTATIONS ORALES.

Ambroise C, Dang M, Govaert G (2013). BP 529 F-60205 Compi{e}gne cedea-France .

Latouche P, Birmele E, Ambroise C (2012). Variational Bayesian inference and complexity control for stochastic block models . Statistical Modelling.

Grandvalet Y, Chiquet J, Ambroise C (2012). Sparsity by Worst-Case Penalties . arXiv preprint arXiv:1210.2077.

Bouaziz M (2012). SHIPS: spectral hierarchical clustering for the inference of population structure . Annals of Human Genetics; NJ, USA. WILEY-BLACKWELL.

McLachlan G (2012). Discriminant analysis . Wiley Interdisciplinary Reviews: Computational Statistics.

Ambroise C, Govaert G (2012). Clustering by maximizing a fuzzy .

Latouche P, Birmele E, Ambroise C, others (2011). Overlapping stochastic block models with application to the french political blogosphere . The Annals of Applied Statistics.

Ambroise C, Matias C (2011). New consistent and asymptotically normal parameter estimates for random-graph mixture models . Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Chiquet J, Grandvalet Y, Ambroise C (2011). Inferring multiple graphical structures . Statistics and Computing.

Jeanmougin M, Guedj M, Ambroise C (2011). Defining a robust biological prior from pathway analysis to drive network inference . arXiv preprint arXiv:1101.3493.

Bouaziz M, Ambroise C, Guedj M (2011). Accounting for population stratification in practice: a comparison of the main strategies dedicated to genome-wide association studies . PloS one.

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Charbonnier C, Chiquet J, Ambroise C (2010). Weighted-LASSO for structured network inference from time course data . Statistical applications in genetics and molecular biology.

Zanghi H, Picard F, Miele V, Ambroise C (2010). Strategies for online inference of model-based clustering in large and growing networks . The Annals of Applied Statistics.

Ambroise C, Matias C (2010). New consistent and asymptotically normal estimators for random graph mixture models . arXiv preprint arXiv:1003.5165.

Zanghi H, Volant S, Ambroise C (2010). Clustering based on random graph model embedding vertex features . Pattern Recognition Letters.

Ambroise C, Dang M (2009). Spatial Data Clustering . Data Analysis.

Chiquet J, Smith A, Grasseau G, Matias C, Ambroise C (2009). Simone: Statistical inference for modular networks . Bioinformatics.

Latouche P, Birmele E, Ambroise C (2009). Overlapping stochastic block models . arXiv preprint arXiv:0910.2098.

Ambroise C, Chiquet J, Matias C, others (2009). Inferring sparse Gaussian graphical models with latent structure . Electronic Journal of Statistics.

Latouche P, Birmele E, Ambroise C (2009). Bayesian methods for graph clustering .

Zanghi H, Picard F, Miele V, Ambroise C (2008). Strategies for online inference of network mixture . Research publication.

Birmele E, Elati M, Rouveirol C, Ambroise C (2008). Identification of functional modules based on transcriptional regulation structure .

Zanghi H, Ambroise C, Miele V (2008). Fast online graph clustering via Erdos--R{e}nyi mixture . Pattern recognition.

Avalos M, Grandvalet Y, Ambroise C (2007). Parsimonious additive models . Computational statistics & data analysis.

Same A, Ambroise C, Govaert G (2007). An online classification EM algorithm based on the mixture model . Statistics and Computing.

Yousfi K, Ambroise C, Cocquerez J, Chevelu J (2006). Supervised learning for guiding hierarchy construction: Application to osteo-articular medical images database .

Zhu X, Ambroise C, McLachlan G (2006). Selection bias in working with the top genes in supervised classification of tissue samples . Statistical Methodology.

Cord A, Ambroise C, Cocquerez J (2006). Feature selection in robust clustering based on Laplace mixture . Pattern Recognition Letters.

Yousfi K, Ambroise C, Cocquerez J, Chevelu J (2006). Driving Hierarchy Construction via Supervised Learning: Application to Osteo-Articular Medical Images Database .

Najjar M, Ambroise C, Cocquerez J, Cotten A, Eltabach M (2006). A Content-Based Image Retrieval System for Osteo-Articular Applications .

Same A, Ambroise C, Govaert G (2006). A classification EM algorithm for binned data . Computational statistics & data analysis.

Jones L, Ng S, Ambroise C, Monico K, Khan N, McLachlan G (2005). Use of Micro Array Data via Model-based Classification in the Study and Prediction of Survival from Lung Cancer . Methods of Microarray Data Analysis.

Charkaoui N, Dubuisson B, Ambroise C, Boatas A (2005). Pattern recognition method for off-board automotive vehicle failure isolation . IFAC Proceedings Volumes.

Avalos M, Grandvalet Y, Ambroise C (2005). P{e}nalisation l1 pour les MAG .

Avalos M, Grandvalet Y, Ambroise C (2005). Model selection via penalization in the additive Cox model .

Avalos M, Grandvalet Y, Ambroise C, others (2005). Discrimination par mod{e}les additifs parcimonieux . Revue d{I}ntelligence Artificielle.

Ziegel E (2005). Data Analysis Tools for DNA Microarrays/Analyzing Microarray Gene Expression Data . Technometrics.

Chaumont L, Yor M, Finkenstadt B, Rootzen H, Franke J, Hardle W, Hafner C, Gentle J, Mori Y, Muller M, others (2005). Books for review If you would like to review a book, and thereby to retain it for your collection, please contact the Book Reviews Editor, whose details can be found by clicking on <U+2018>books currently available<U+2019>in the information on the Royal Statistical Society<U+2019>s Web site .

McLachlan G, Do K, Ambroise C (2005). Analyzing microarray gene expression data .

Govaert G, Ambroise C (2005). Analyse de Donn{e}es et Data Mining . Polycopi{e} de cours, Universit{e} de Technologie de Compi{e}gne.

Same A, Govaert G, Ambroise C (2005). A mixture model-based on-line CEM algorithm .

Charkaoui N, Dubuisson B, Ambroise C, Millemann S (2005). A decision tree classifier for vehicle failure isolation . WIT Transactions on Information and Communication Technologies.

Avalos M, Grandvalet Y, Ambroise C (2004). Penalized additive logistic regression for cardiovascular risk prediction .

McLachlan G, Chang S, Mar J, Ambroise C (2004). On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples .

Hamdan H, Govaert G, Ambroise C, Herve C (2004). Mixture model approach for acoustic emission control of pressure equipment .

McLachlan G, Do K, Ambroise C (2004). Microarrays in gene expression studies . Analyzing Microarray Gene Expression Data.

Avalos M, Grandvalet Y, Ambroise C (2004). G{e}n{e}ralisation du lasso aux modeles additifs .

Same A, Ambroise C, Govaert G (2004). A mixture model approach for on-line clustering .

Avalos M, Grandvalet Y, Ambroise C (2003). Regularization methods for additive models .

Najjar M, Ambroise C, Cocquerez J (2003). Image retrieval using mixture models and em algorithm .

Najjar M, Ambroise C, Cocquerez J (2003). Feature selection for semisupervised learning applied to image retrieval .

Same A, Ambroise C, Govaert G (2003). A mixture model approach for binned data clustering .

Ambroise C, McLachlan G (2002). Selection bias in gene extraction on the basis of microarray gene-expression data . Proceedings of the national academy of sciences.

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Ambroise C, Govaert G (2002). Clustering and Models .

Najjar M, Cocquerez J, Ambroise C (2002). A Semi-supervised Learning Approach to Image Retrieval .

Grandvalet Y, Ambroise C, others (2001). Semi-supervised marginboost .

d'Alche-Buc F, Grandvalet Y, Ambroise C (2001). Semi-supervised marginboost . Advances in neural information processing systems.

Ambroise C, Grandvalet Y (2001). Prediction of ozone peaks by mixture models . Ecological modelling.

Ambroise C, Denoeux T, Govaert G, Smets P (2001). Learning from an imprecise teacher: probabilistic and evidential approaches . Applied Stochastic Models and Data Analysis.

Grandvalet Y, d<U+2019>Alche-Buc F, Ambroise C (2001). Boosting mixture models for semi-supervised learning .

Ambroise C, Seze G, Badran F, Thiria S (2000). Hierarchical clustering of self-organizing maps for cloud classification . Neurocomputing.

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Ambroise C, Govaert G (2000). EM algorithm for partially known labels .

Ambroise C, Govaert G (2000). Clustering by maximizing a fuzzy classification maximum likelihood criterion .

Ambroise C (1998). The EM Algorithm and Extensions, by GM McLachlan and T. Krishnan . Journal of Classification.

Ambroise C, Govaert G (1998). Convergence of an EM-type algorithm for spatial clustering . Pattern recognition letters.

Ambroise C, Govaert G (1998). An iterative algorithm for spatial clustering .

Ambroise C, Dang M, Govaert G (1997). Clustering of spatial data by the EM algorithm .

Ambroise C, Govaert G (1996). Constrained clustering and Kohonen self-organizing maps . Journal of Classification.

Ambroise C (1996). Approche probabiliste en classification automatique et contraintes de voisinage .

Ambroise C (1996). Analyzing dissimilarity matrices via Kohonen maps .