Nos thèmes de recherche:

• Tests multiples, Statistique pour la génétique et la génomique, études d’association
• Apprentissage en grande dimension
• Sélection de modèles et méthodes de régularisation
• Structures latentes en science du vivant
• Analyse de sensibilité, Inégalités oracles, Construction de méta-modèles
• Analyse de données de survie
• Analyse de données fonctionnelles, parcimonie fonctionnelle
• Classification géométrique des courbes multivariées issues de la langue des signes
• Détection de ruptures
• Quantification des incertitudes en présence de données manquantes
• Evolution du génome et l’adaptation des espèces, Modèle d'évolution de séquences, Gènes dupliqués, éléments transposables et évolution des génomes.
• Bases de données pour la génomique et les éléments transposables
• Modèles de graphes aléatoires pour l'apprentissage en grande dimension, et Inférence des réseaux,
• Statistiques des processus stochastiques avec des applications en sciences du vivant.
• Processus de branchement
• Intégration de données cliniques longitudinales
• Screening
• Intelligence Artificielle fiable
• Détection des signaux et des promoteurs chez les plantes
• Mise en package de pipeline pour la génomique
• Méthodes à noyaux

Voir les publications de l'équipe.

L’équipe de Statistique et Génome développe ses activités de recherche dans les domaines de l’apprentissage statistique, des statistiques appliquées, de la statistique mathématique et de la bio-informatique.

Notre objectif est de développer des méthodes originales pour l’analyse de données issues des sciences du vivant.

Les recherches portent sur des développements méthodologiques, mathématiques et bio-informatiques pour des modèles issus des sciences du vivant (génomique, santé, agronomie, agro-écologie, hydrologie, analyse du mouvement, etc.).

Nous travaillons sur l’analyse des gènes dupliqués, les procédures de tests multiples, l’analyse de données fonctionnelles, et les inégalités oracles dans un contexte d’analyse de sensibilité. L’équipe développe également des logiciels pour la communauté scientifique, disponibles publiquement.

L’équipe est composée d’enseignants-chercheurs et chercheurs en statistique, machine learning et bio-informatique.

Le numéro de téléphone complet est composé du préfixe commun : +33 (0)1 64 85 et du numéro de poste indiqué dans le tableau.

Quelques récentes distinctions.

L'équipe est très impliquée (direction et cours) dans le Master Data Science Santé Assurance Finance.

Ce master 2 en alternance et en formation initiale propose une formation généraliste en statistique et apprentissage statistique, destinée aux étudiants en mathématiques appliquées et aux ingénieurs souhaitant se spécialiser en science des données.

Grâce à des cours d'informatique (programmation et bases de données), les étudiants acquièrent les compétences indispensables en statistique et informatique pour devenir data scientists.

Les évaluations, en grande partie sous forme de projets, développent la capacité à mettre en œuvre des méthodes et algorithmes, ainsi qu’à gérer des projets en équipe. La formation inclut également des cours spécialisés en santé, assurance et finance, offrant une expertise approfondie sur les données complexes de ces secteurs.

• Le séminaire principal de l'équipe Statistique et Génome
• Le séminaire interdisciplinaire (math. et génomique).
• Le séminaire Statistiques et probabilités

• Toby Hocking, Tenured Associate Professor, Université de Sherbrooke, Département d’informatique.

• L’équipe Statistique et Génome a établie une forte collaboration avec l'équipe Réseaux Génomiques de l'Institut de Sciences des Plantes-Paris Saclay ( IPS2) depuis une dizaine d'années.
• Genhotel
• Lancaster
  • Anica Kostic
  • Gaetano Romano
  • Paul Fearnhead

• 2023- : ANR TraitZoo, Responsable Lamme : Ambroise Christophe, Description : Biogéographie des traits et diversité fonctionnelle du mésozooplancton marin : données à haut débit (imagerie, -omique), apprentissage machine, et modélisation numérique avec INRIA and Paris Centre Univ.

• 2022- : Projet BCI ŸNFABRE Responsable Lamme : Ambroise Christophe, Description : Consacré à la génétique de la larve du scarabée Tenebrio Molitor avec Amine Madoui (CEA).

• 2022- : CIFRE Sensorion Responsable Lamme : Christophe Ambroise, Marie-Szafranski, Description : Stratification de patients et analyse de biomarqueurs diagnostiques dans un espace de Hilbert à noyau consensus reproduisant : application au traumatisme sonore aigu.

• 2022-2023 : IDOOH (PHC Van Gogh) Responsable LaMME : Marie Szafranski, Description : Coordination. Projet sur l’intégration de données omiques hétérogènes impliquant le LaMME et le Swammerdam Institute for Life Sciences (University of Amsterdam).

• 2021- : ANR TOCCATA Responsable Lamme : Guillem Rigaill, Description : WP leader, le coordinateur est W. Majeran (IPS2). Regulatory mechanisms of C4-differentiation : make CO2 fixation great again.

• 2021- : DIGIT-BIO Peersim Responsable Lamme : Guillem Rigaill, Description : Coordinateur avec E. Delannoy (IPS2). Planification d’expériences pour l’Etude de la Réponse aux Stress-multiples et l’Intégration Multi-omique. Méta-programme DIGITBIO.

• 2020- : Quantmetry, Responsable Lamme : Nicolas Brunel, Description : Projet de collaboration avec Quantmetry.

• 2019- : CIFRE Dassault Systèmes Responsable Lamme : Guilloux Agathe, Description : Machine Learning applied to healthcare databases avec Paris Centre Univ. and Evry Paris Saclay univ.

• 2019- : CIFRE CEMKA Responsable Lamme : Guilloux Agathe, Description : Machine Learning applied to healthcare databases avec Paris Centre Univ. and Evry Paris Saclay Univ.

• 2019- : DISAITEK Responsable Lamme : Nicolas Brunel, Description : Projet de recherche en collaboration avec DISAITEK.

• 2018- : ATIGE Responsable Lamme : Guillem Rigaill, Description : Coordinateur. Modèles de segmentation pour l’étude des régulations de l’ADN et de l’ARN. Application à l’analyse des données de Chip-Seq et à l’étude des PPR.

• 2018- : CIFRE Enterome Responsable Lamme : Christophe Ambroise, Description : Prise en compte de l’organisation hiérarchique des espèces pour la découverte de signatures métagénomiques multi-échelles.

• 2017- : ANR EPITREE, Responsable Lamme : Ambroise Christophe, Description : Evolutionary and functional relevance of epigenetic variations in forest trees, avec des chercheurs INRA d’Orléans, Bordeau et Clermont-Ferrand. URL : EPITREE

• 2017- : Projet Antibio-résistance Responsable Lamme : Ambroise Christophe, Description : Financement d’un post-doctorant sur 2 ans pour participer au développement d’une application de téléphone mobile pour le diagnostic de l’antibio-résistance dans les camps de réfugiés MSF. Les fonds étaient issus des dons à la fondation MSF. Le prototype d’Antibiogo (logiciel maintenant cédé à Médecins Sans Frontières et Google), a remporté, parmi 2602 candidats, le premier prix du Google.org AI Impact Challenge. C. Ambroise a supervisé ce projet et participé au développement pendant trois ans.

• 2016- : ANR MemoDeep Responsable Lamme : Ambroise Christophe, Description : Methods and Models for Deep Screening of subphenotypes in Parkinson’s Disease avec une équipe INSERM de Toulouse, une équipe de l’institut du cerveau et de la moelle.

• 2016- : CIFRE bioptimize Responsable Lamme : Christophe Ambroise, Description : Développements méthodologiques autour des modèles mixtes additifs généralisés pour la recherche de signature moléculaires de cancer du sein triple négatif.

• 2015- : France social security Responsable Lamme : Guilloux Agathe, Description : Machine Learning applied to healthcare databases avec Ecole polytechnique (Caisse Nationale d’Assurance Maladie, CNAM).

• 2015- : CIFRE PwC Responsable Lamme : Guilloux Agathe, Description : Machine Learning applied to the insurance sector avec Sorbonne Univ.

Les anciens membres sont ordonnés des plus récents aux plus anciens.

La liste suivante de publications est extraite automatique de HAL. Elle n'est pas complète, partiellement fausse. Pour une liste plus fiable considérer la page de chacun des membres.

2025
[1]	de Santiago, K. From stratification to prediction : multimodal machine learning with latent block models and mixtures of experts. , 2025. (Theses)
[2]	Ben Alaya, M., Ngo, T.B.T. & Pergamenchtchikov, S. Truncated sequential guaranteed estimation for the Cox-Ingersoll-Ross models. , 2025., (working paper or preprint).
[3]	Le, V.a. & Ngo, T.B.T. Evaluating the air quality transmission among Southeast Asian cities. , 2025., (working paper or preprint).
2024
[4]	Guedj, O. Linear regression in low and high dimension. , 2024. (Theses)
[5]	Chassat, P. Functional and Shape Data Analysis under the Frenet-Serret Framework : Application to Sign Language Motion Trajectories Analysis. , 2024. (Theses)
[6]	Pluntz, M., Dalmasso, C., Tubert-Bitter, P. & Ahmed, I. A Simple Information Criterion for Variable Selection in High-Dimensional Regression. Statistics in Medicine, Wiley-Blackwell, 2024.
[7]	Bondi, A., Pulido, S. & Scotti, S. The rough Hawkes Heston stochastic volatility model. Mathematical Finance, 34(4):1197-1241, Wiley, 2024.
[8]	Lesur Kupin, I., Rogier, O., Sow, M.D., Boury, C., Duplan, A., Garnier, A., Senhaji-Rachik, A., Civan, P., Daron, J., Delaunay, A., Duvaux, L., Benoit, V., Guichoux, E., Le Provost, G., Sanou, E., Ambroise, C., Plomion, C., Salse, J., Segura, V., Tost, J. & Maury, S. A Strategy for Studying Epigenetic Diversity in Natural Populations: Proof of Concept in Poplar and Oak. Journal of Experimental Botany, 75(18):5568-5584, Oxford University Press (OUP), 2024., (We thank the Genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, https://doi.org/10.15454/1.5572369328961167E12) for providing computing and storage resources. WGS and WGBS sequencing were performed at the Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Evry, France. All the capture experiments were performed at the PGTB (doi:10.15454/1.5572396583599417E12). SeqCapBis sequencing was performed at the GeT-PlaGe core facility, INRAe Toulouse.).
[9]	Archambault, T., Filoche, A., Charantonis, A.A., Bereziat, D. & Thiria, S. Learning Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations. Journal of Advances in Modeling Earth Systems, 16(6):e2023MS004047, American Geophysical Union, 2024.
[10]	Truong, C. & Runge, V. An Efficient Algorithm for Exact Segmentation of Large Compositional and Categorical Time Series. Stat, 13(4):e70012, John Wiley \& Sons, 2024.
[11]	Alamichel, C., Calvo, J., Hingant, E., Latrach, S., Quiblier, N. & Yvinec, R. Modeling compartmentalization within intracellular signaling pathway. ESAIM: Proceedings and Surveys, 77:100-122, EDP Sciences, 2024.
[12]	Blot, V., de Brionne, A.L., Sellami, I., Trassard, O., Beau, I., Sonigo, C. & Brunel, N.J.B. Efficient Precision Control in~Object Detection Models for~Enhanced and~Reliable Ovarian Follicle Counting. In International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 15167:183-193, Springer Nature Switzerland, 2024.
[13]	Archambault, T., Filoche, A., Charantonis, A.A. & Bereziat, D. Pre-training and Fine-tuning Attention Based Encoder Decoder Improves Sea Surface Height Multi-variate Inpainting. In VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications, 2024.
[14]	De Santiago, K., Szafranski, M. & Ambroise, C. Intégration tardive de données multimodales par modèles à blocs stochastiques. In 55e Journées de Statistique de la SFdS, 2024.
[15]	Samson, F. & Aubourg, S. GBOT one flew over the ortholog's nest. , 2024., (Poster).
[16]	Mary-Huard, T. & Rigaill, G. fst4pg: Genetic Distance Segmentation for Population Genetics. , 2024., ().
[17]	Jawad, H. & Brunel, N.J.B. QROA: A Black-Box Query-Response Optimization Attack on LLMs. , 2024., (working paper or preprint).
[18]	Medernach, D., Lemaire, C., Girousse, E., Keisler, J., Richon, J. & Brunel, N. Flood prediction challenge. , 2024., (working paper or preprint).
[19]	Lelievre, T., Pavliotis, G.A., Robin, G., Santet, R. & Stoltz, G. Optimizing the diffusion coefficient of overdamped Langevin dynamics. , 2024., (working paper or preprint).
[20]	Dedecker, J., Guedj, O. & Taupin, M.L. Asymptotic confidence interval for R2 in multiple linear regression. , 2024., (working paper or preprint).
2023
[21]	Szafranski, M. Apprentissage machine pour l'intégration et l'interaction de données structurées. , 2023.
[22]	Nevoret, C. Analyse de données longitudinales, causalité et parcours de soin : une application aux bases médico-administratives françaises. , 2023. (Theses)
[23]	Ibrahim Amoukou, S. Trustworthy machine learning : explainability and distribution-free uncertainty quantification. , 2023. (Theses)
[24]	Sanou, D.E. Integration of complex omics data through Multiscale Gaussian Graphical Models. , 2023. (Theses)
[25]	Obry, L. Procédures de tests multiples avec pondérations dans les études d'association pangénomiques. , 2023. (Theses)
[26]	Chen, S., Brunel, N.J.B., Yang, X. & Cui, X. Learning Interactions in Reaction Diffusion Equations by Neural Networks. Entropy, 25(3):489, MDPI, 2023.
[27]	Cousien, A., Dhersin, J.S., Tran, V.C. & Vo, T.P.T. Respondent-driven sampling on sparse Erdös-Rényi graphs. Acta Mathematica Vietnamica, 48(3):479-513, Springer Singapore, 2023.
[28]	Laso-Jadart, R., O'Malley, M., Sykulski, A.M., Ambroise, C. & Madoui, M.A. Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations.. BMC Ecology and Evolution, 23(1):46, BMC, 2023.
[29]	Obry, L. & Dalmasso, C. Weighted multiple testing procedures in genome-wide association studies. PeerJ, 11:e15369, PeerJ, 2023.
[30]	de Rosa, L., Latocca, M. & Stefani, G. ON DOUBLE HÖLDER REGULARITY OF THE HYDRODYNAMIC PRESSURE IN BOUNDED DOMAINS. Calculus of Variations and Partial Differential Equations, 62(3):85, Springer Verlag, 2023.
[31]	de Rosa, L., Latocca, M. & Stefani, G. Full Double Hölder Regularity of the Pressure in Bounded Domains. International Mathematics Research Notices, Oxford University Press (OUP), 2023.
[32]	Runge, V., Hocking, T.D., Romano, G., Afghah, F., Fearnhead, P. & Rigaill, G. gfpop : An R Package for Univariate Graph-Constrained Change-Point Detection. Journal of Statistical Software, 106(6):1-39, University of California, Los Angeles, 2023.
[33]	Lelievre, T., Robin, G., Sekkat, I., Stoltz, G. & Victorino Cardoso, G. Generative methods for sampling transition paths in molecular dynamics. ESAIM: Proceedings, 73:238-256, EDP Sciences, 2023.
[34]	Tran, H.C., Schmitt, V., Lama, S., Wang, C., Launay-Avon, A., Bernfur, K., Sultan, K., Khan, K., Brunaud, V., Liehrmann, A., Castandet, B., Levander, F., Rasmusson, A., Mireau, H., Delannoy, E. & van Aken, O. An mTRAN-mRNA interaction mediates mitochondrial translation initiation in plants. Science, 381(6661), American Association for the Advancement of Science (AAAS), 2023.
[35]	Romano, G., Eckley, I., Fearnhead, P. & Rigaill, G. Fast Online Changepoint Detection via Functional Pruning CUSUM statistics. Journal of Machine Learning Research, 24:1-36, Microtome Publishing, 2023.
[36]	Sloyan, B., Chapman, C., Cowley, R. & Charantonis, A.A. Application of Machine Learning Techniques to Ocean Mooring Time Series Data. Journal of Atmospheric and Oceanic Technology, 40(3):241-260, American Meteorological Society, 2023.
[37]	Lerbet, J., Challamel, N., Nicot, F. & Darve, F. Intrinsic incremental evolution of hypoelastic discrete mechanical systems. Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 103(11), Wiley-VCH Verlag, 2023.
[38]	Liehrmann, A., Delannoy, E., Launay-Avon, A., Gilbault, E., Loudet, O., Castandet, B. & Rigaill, G. DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection. NAR Genomics and Bioinformatics, 5(4):lqad098, Oxford University Press, 2023.
[39]	Brunel, N.J.B., Cordier, T., Blot, V., Lacombe, L., Morzadec, T. & Capitaine, A. Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library. In Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 204, 2023.
[40]	Chassat, P., Park, J. & Brunel, N.J.B. Shape Analysis of Euclidean Curves under Frenet-Serret Framework. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 4004-4013, IEEE, 2023.
[41]	Filoche, A., Brajard, J., Charantonis, A.A. & Bereziat, D. Learning 4DVAR inversion directly from observations. In Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS), International Conference on Computational Science (ICCS), 2023.
[42]	Archambault, T., Filoche, A., Charantonis, A.A. & Bereziat, D. Multimodal Unsupervised Spatio-Temporal Interpolation of satellite ocean altimetry maps. In Proceedings of the 18th International Conference on Computer Vision Theory and Applications, 2023.
[43]	Bleistein, L., Fermanian, A., Jannot, A.S. & Guilloux, A. Learning the Dynamics of Sparsely Observed Interacting Systems. In PMLR - Proceedings of Machine Learning Research, 202, 2023.
[44]	Bleistein, L. & Guilloux, A. On the Generalization Capacities of Neural Controlled Differential Equations. In Workshop on New Frontiers in Learning, Control, and Dynamical Systems at the International Conference on Machine Learning (ICML 2023), 2023.
[45]	Samson, F. & Rizzon, C. A long story of genomic interface. , 2023., (Poster).
[46]	De Santiago, K., Szafranski, M. & Ambroise, C. Mixture of stochastic block models for multiview clustering. In ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 151-156, 2023.
[47]	Pishchagina, L., Rigaill, G. & Runge, V. Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series. , 2023., (working paper or preprint).
[48]	Liehrmann, A. & Rigaill, G. Ms.FPOP: An Exact and Fast Segmentation Algorithm With a Multiscale Penalty. , 2023., (38 pages, 17 figures).
[49]	Ibrahim Amoukou, S. & Brunel, N.J.B. Adaptive Conformal Prediction by Reweighting Nonconformity Score. , 2023., (working paper or preprint).
[50]	Sow, M.D., Rogier, O., Lesur, I., Daviaud, C., Mardoc, E., Sanou, E., Duvaux, L., Civan, P., Delaunay, A., Descauses, M.C.L., Benoit, V., Le-Jan, I., Buret, C., Besse, C., Durufle, H., Fichot, R., Le-Provost, G., Guichoux, E., Boury, C., Garnier, A., Senhaji-Rachik, A., Jorge, V., Ambroise, C., Tost, J., Plomion, C., Segura, V., Maury, S. & Salse, J. Epigenetic Variation in Tree Evolution: a case study in black poplar (Populus nigra). , 2023., (working paper or preprint).
2022
[51]	Massoumi, S., Challamel, N., Lerbet, J., Wautier, A., Nicot, F. & Darve, F. Static bending of granular beam: exact discrete and nonlocal solutions. Meccanica, 57(8):2043-2066, Springer Verlag, 2022.
[52]	Kamari, H., Huet, S. & Taupin, M.L. RKHSMetaMod: An R Package to Estimate the Hoeffding Decomposition of a Complex Model by Solving RKHS Ridge Group Sparse Optimization Problem. The R Journal, 14(1):101-122, R Foundation for Statistical Computing, 2022.
[53]	Amorino, C., Dion, C., Gloter, A. & Lemler, S. On the nonparametric inference of coefficients of self-exciting jump-diffusion. Electronic Journal of Statistics , 16, Shaker Heights, OH : Institute of Mathematical Statistics, 2022.
[54]	Lerbet, J., Challamel, N., Nicot, F. & Darve, F. Divergence instability of kinematically constrained Hencky chains: Analytic results and asymptotic behavior. Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 102(1), Wiley-VCH Verlag, 2022.
[55]	Challamel, N., Nicot, F., Wautier, A., Darve, F. & Lerbet, J. Hyperelastic or Hypoelastic Granular Circular Chain Instability in a Geometrically Exact Framework. Journal of Engineering Mechanics - ASCE, 148(9):04022053, American Society of Civil Engineers, 2022.
[56]	Bichat, A., Ambroise, C. & Mariadassou, M. Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process. Computational Statistics, 37(3):995-1013, Springer Verlag, 2022.
[57]	Ben Alaya, M., Kebaier, A. & Ngo, T.B.T. Central limit theorem for the antithetic multilevel Monte Carlo method. The Annals of Applied Probability, 32(3), Institute of Mathematical Statistics (IMS), 2022.
[58]	Park, J., Ahn, J. & Jeon, Y. Sparse functional linear discriminant analysis. Biometrika, 109(1):209-226, Oxford University Press (OUP), 2022.
[59]	Cottin, A., Pecuchet, N., Zulian, M., Guilloux, A. & Katsahian, S. IDNetwork: A deep illness-death network based on multi-state event history process for disease prognostication. Statistics in Medicine, 41(9):1573-1598, Wiley-Blackwell, 2022.
[60]	Bernis, G., Brunel, N.J.B., Kornprobst, A. & Scotti, S. Stochastic evolution of distributions and functional Bollinger bands. Applied Stochastic Models in Business and Industry, 38(2):370-390, Wiley, 2022.
[61]	Courbariaux, M., De Santiago, K., Dalmasso, C., Danjou, F., Bekadar, S., Corvol, J.C., Martinez, M., Szafranski, M. & Ambroise, C. A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping. Frontiers in Genetics, 13, Frontiers Media, 2022.
[62]	Maillot, G., Szadkowski, E., Massire, A., Brunaud, V., Rigaill, G., Caromel, B., Chad\oeuf, J., Bachellez, A., Touhami, N., Hein, I., Lamour, K., Balzergue, S. & Lefebvre, V. Strive or thrive: Trends in Phytophthora capsici gene expression in partially resistant pepper. Frontiers in Plant Science, 13(980587):1-19, Frontiers, 2022.
[63]	Sundqvist, M., Chiquet, J. & Rigaill, G. Adjusting the adjusted Rand Index A multinomialstory. Computational Statistics, 38(1):327-347, Springer Verlag, 2022.
[64]	Hocking, T.D., Rigaill, G., Fearnhead, P. & Bourque, G. Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data. Journal of Statistical Software, 101(10), University of California, Los Angeles, 2022.
[65]	Javelot, H., Meyer, G., Becker, G., Post, G., Runge, V., Pospieszynski, P., Schneiderlin, T., Armand-Branger, S., Michel, B., Weiner, L., Faria, C.G.F., Drapier, D., Fakra, E., Fossati, P., Haffen, E., Yrondi, A. & Hingray, C. Les échelles anticholinergiques : usage en psychiatrie et mise à jour de l'échelle d'imprégnation anticholinergique. L'Encéphale, Elsevier Masson, 2022.
[66]	Xiao, Z., Brunel, N.J.B., Tian, C., Guo, J., Yang, Z. & Cui, X. Constrained Nonlinear and Mixed Effects Integral Differential Equation Models for Dynamic Cell Polarity Signaling. Frontiers in Plant Science, 13, Frontiers, 2022.
[67]	Alvarez-Diaz, J., Richard, M., Thareau, V., Teano, G., Paysant-Le-Roux, C., Rigaill, G., Pflieger, S., Gratias, A. & Geffroy, V. Genome-Wide Identification of Key Components of RNA Silencing in Two Phaseolus vulgaris Genotypes of Contrasting Origin and Their Expression Analyses in Response to Fungal Infection. Genes, 13(1):1-19, MDPI, 2022.
[68]	Antonsanti, P.L., Benseghir, T., Jugnon, V., Ghosn, M., Chassat, P., Kaltenmark, I. & Glaunes, J. How to Register a Live onto a Liver ? Partial Matching in the Space of Varifolds. Journal of Machine Learning for Biomedical Imaging, Melba editors, 2022., (30 pages, 11 figures, Special Issue: Information Processing in Medical Imaging (IPMI) 2021, Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org).
[69]	Guinot, F., Szafranski, M. & Ambroise, C. Compression structurée de l'information génétique et étude d'association pangénomique par modèles additifs. In Intégration de données biologiques, pages 129-163, ISTE Group, 2022.
[70]	Brunel, N.J.B. & Ibrahim Amoukou, S. Consistent Sufficient Explanations and Minimal Local Rules for explaining any classifier or regressor. In Neural Information Processing Systems, 2022.
[71]	Chassat, P., Park, J. & Brunel, N.J.B. Analysis of variability in sign language hand trajectories: development of generative model. In MOCO '22: Proceedings of the 8th International Conference on Movement and Computingpages 1-8, ACM, 2022.
[72]	Frioux, C., Huet, S., Labarthe, S., Martinelli, J., Malou, T., Sherman, D.J., Taupin, M.L. & Salas, P.U. Dynamic Flux Balance Analysis with Metamodels. , 2022., (Poster).
[73]	Lefebvre, V., Massire, A., Maillot, G., Szadkowski, E., Caromel, B., Bachellez, A., Touhami, N., Brunaud, V., Rigaill, G. & Lamour, K. Impact de la résistance partielle du piment sur l'expression des gènes de Phytophthora capsici : conséquences pour la création variétale. , 2022., (Poster).
[74]	Frioux, C., Huet, S., Labarthe, S., Martinelli, J., Malou, T., Sherman, D.J., Taupin, M.L. & Ugalde-Salas, P. Dynamic Flux Balance Analysis with Metamodels. , 2022., (Poster).
[75]	Charrier, T., Fresneau, B., Haddy, N., Schwartz, B., Journy, N., Demoor-Goldschmidt, C., Diallo, I., Surun, A., Aerts, I., Doz, F., Souchard, V., Vu-Bezin, G., Lemler, S., Letort, V., Rubino, C., Fresneau, B., Haddy, N., Schwartz, B., Journy, N., Demoor-Goldschmidt, C., Diallo, I., Surun, A., Aerts, I., Doz, F., Souchard, V., Vu-Bezin, G., Letort, V., Rubino, C., de Vathaire, F., Latouche, A. & Allodji, R.S. Increased Cardiac Risk After a Second Malignant Neoplasm Among Childhood Cancer Survivors, a FCCSS Study. , 2022., (Poster).
[76]	Ibrahim Amoukou, S. & Brunel, N.J.B. Local and Regional Counterfactual Rules: Summarized and Robust Recourses. , 2022., (ICML (International Conference on Machine Learning) 2023 Workshop on Counterfactuals in Minds and Machines).
[77]	Park, J., Brunel, N.J.B. & Chassat, P. Curvature and Torsion estimation of 3D functional data: A geometric approach to build the mean shape under the Frenet Serret framework. , 2022., (Major revision of the previous version: new motivating example, extended methodology and simulation studies, globally restructured and rewritten. arXiv admin note: substantial text overlap with arXiv:1910.12049).
2021
[78]	Pascucci, M., Royer, G., Adamek, J., Asmar, M.A., 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, J.P., Ambroise, C. & Madoui, M.A. AI-based mobile application to fight antibiotic resistance. Nature Communications, 12(1):1173, Nature Publishing Group, 2021.
[79]	Liehrmann, A., Rigaill, G. & Hocking, T.D. Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models. BMC Bioinformatics, 22(1), BioMed Central, 2021.
[80]	Guilcher, M., Liehrmann, A., Seyman, C., Blein, T., Rigaill, G., Castandet, B. & Delannoy, E. Full Length Transcriptome Highlights the Coordination of Plastid Transcript Processing. International Journal of Molecular Sciences, 22(20):1-15, MDPI, 2021.
[81]	Bianco, B., Francois-Garret, B., Butin, M., Dalmasso, C., Casagrande, F., Mokhtari, M. & Eleni Dit Trolli, S. Procalcitonin in Preterm Neonates: A Different Threshold and Prolonged Interpretation. Frontiers in Pediatrics, 9, Frontiers, 2021.
[82]	Dalmasso, C., Derbois, C., Veyssiere, M., Olaso, R., Lamacchia, C., Alpizar-rodriguez, D., Deleuze, J.f., Finckh, A. & Petit-Teixeira, E. Identification of biological pathways specific to phases preceding rheumatoid arthritis development through gene expression profiling. International Journal of Immunogenetics, Wiley, 2021.
[83]	Lerbet, J., Challamel, N., Nicot, F. & Darve, F. Divergence instability of kinematically constrained Hencky chains: Analytic results and asymptotic behavior. Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 102(1), Wiley-VCH Verlag, 2021.
[84]	Eastman, M., Parry, S., Sefton, C., Park, J. & England, J. Reconstructing Spatiotemporal Dynamics in Hydrological State Along Intermittent Rivers. Water, 13(4):493, MDPI, 2021.
[85]	Kim, D., Bayad, A. & Park, S.h. Iterated stable numbers and iterated amicable pairs. Mathematical Methods in the Applied Sciences, 44(17):12479-12499, Wiley, 2021.
[86]	Correa, M., Lerat, E., Birmele, E.E., Samson, F., Bouillon, B., Normand, K. & Rizzon, C. The Transposable Element Environment of Human Genes Differs According to Their Duplication Status and Essentiality. Genome Biology and Evolution, 13(5), Society for Molecular Biology and Evolution, 2021., (Erratum: https://doi.org/10.1093/gbe/evab175).
[87]	Sanchez Garcia de La Torre, V., Majorel-Loulergue, C., Rigaill, G.J., Gonzalez, D.A., Soubigou-Taconnat, L., Pillon, Y., Barreau, L., Thomine, S., Fogliani, B., Burtet-Sarramegna, V. & Merlot, S. Wide Cross-species RNA-Seq Comparison Reveals Convergent Molecular Mechanisms Involved in Nickel Hyperaccumulation Across Dicotyledons. New Phytologist, 229(2):994-1006, Wiley, 2021.
[88]	Bussy, S., Alaya, M., Jannot, A.s. & Guilloux, A. Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics. Biometrics, 78(4):1414-1426, Wiley, 2021.
[89]	Dieuleveut, A., Fort, G., Moulines, E. & Robin, G. Federated Expectation Maximization with heterogeneity mitigation and variance reduction. In Advances in Neural Information Processing Systems 35 pre-proceedings (NeurIPS 2021), 2021.
[90]	Frioux, C., Huet, S., Labarthe, S., Martinelli, J., Malou, T., Sherman, D., Taupin, M.L. & Ugalde-Salas, P. Accelerating metabolic models evaluation with statistical metamodels: application to Salmonella infection models. In ESAIM: Proceedings and Surveys, 73:187-217, 2021.
[91]	Landes, C., Diaz-Lazcoz, Y., Henaut, A. & Torresani, B. Pseudo-Rate Matrices, beyond Dayhoff's model. , 2021., (working paper or preprint).
2020
[92]	Kamari, H. Predictive quality of meta-models constructed on the reproducing kernel Hilbert spaces and sensitivity analysis of complex models.. , 2020. (Theses)
[93]	Bichat, A. Prise en compte de l'organisation hiérarchique des espèces pour la découverte de signatures métagénomiques multi-échelles.. , 2020. (Theses)
[94]	Guinot, F., Szafranski, M., Chiquet, J., Zancarini, A., Le Signor, C., Mougel, C. & Ambroise, C. Fast computation of genome-metagenome interaction effects. Algorithms for Molecular Biology, 15(1):art.13 (21p.), BioMed Central, 2020.
[95]	Tham, D.S.Y., Sowden, P., Grandison, A., Franklin, A., Lee, A.K.W., Ng, M., Park, J., Pang, W. & Zhao, J. A systematic investigation of conceptual color associations.. Journal of Experimental Psychology: General, 149(7):1311-1332, American Psychological Association, 2020.
[96]	Billat, V., Palacin, F., Correa, M. & Pycke, J.R. Pacing Strategy Affects the Sub-Elite Marathoner's Cardiac Drift and Performance. Frontiers in Psychology, 10:3026, Frontiers Media, 2020.
[97]	Laso-jadart, R., Sugier, K., Petit, E., Labadie, K., Peterlongo, P., Ambroise, C., Wincker, P., Jamet, J.L. & Madoui, M.a. Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866). Ecology and Evolution, 10(16):8894-8905, Wiley Open Access, 2020.
[98]	Lallemand, T., Leduc, M., Landes, C., Rizzon, C. & Lerat, E. An Overview of Duplicated Gene Detection Methods: Why the Duplication Mechanism Has to Be Accounted for in Their Choice. Genes, 11(9):1046, MDPI, 2020.
[99]	Gautreau, G., Bazin, A., Gachet, M., Planel, R., Burlot, L., Dubois, M., Perrin, A., Medigue, C., Calteau, A., Cruveiller, S., Matias, C., Ambroise, C., Rocha, E.P.C. & Vallenet, D. PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph. PLoS Computational Biology, PLOS, 2020.
[100]	Hulot, A., Chiquet, J., Jaffrezic, F. & Rigaill, G. Fast tree aggregation for consensus hierarchical clustering. BMC Bioinformatics, 21(1), BioMed Central, 2020.
[101]	Laso-Jadart, R., Ambroise, C., Peterlongo, P. & Madoui, M.A. metaVaR: introducing metavariant species models for reference-free metagenomic-based population genomics. PLoS ONE, Public Library of Science, 2020.
[102]	Tu, C.Y., Park, J. & Wang, H. Estimation of Functional Sparsity in Nonparametric Varying Coefficient Models for Longitudinal Data Analysis. Statistica Sinica, Taipei : Institute of Statistical Science, Academia Sinica, 2020.
[103]	Romano, G., Rigaill, G., Runge, V. & Fearnhead, P. Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise. Journal of the American Statistical Association, pages 1-16, Taylor \& Francis, 2020.
[104]	Momal, R., Robin, S. & Ambroise, C. Tree-based inference of species interaction networks from abundance data. Methods in Ecology and Evolution, 11(5):621-632, Wiley, 2020.
[105]	Lerbet, J., Challamel, N., Nicot, F. & Darve, F. Second-order work criterion and divergence criterion: a full equivalence for kinematically constrained systems. Mathematics and Mechanics of Complex Systems, 8(1):1-28, International Research Center for Mathematics \& Mechanics of Complex Systems (M\&MoCS),University of L'Aquila in Italy, 2020.
[106]	Frouin, A., Dandine-Roulland, C., Pierre-Jean, M., Deleuze, J.F., Ambroise, C. & Le Floch, E. Exploring the link between additive heritability and prediction accuracy from a ridge regression perspective. Frontiers in Genetics, 11:581594, Frontiers Media, 2020.
[107]	Bichat, A., Plassais, J., Ambroise, C. & Mariadassou, M. Incorporating Phylogenetic Information in Microbiome Differential Abundance Studies Has No Effect on Detection Power and FDR Control. Frontiers in Microbiology, 11, Frontiers Media, 2020.
[108]	Lavi, I., Meunier, N., Voituriez, R. & Casademunt, J. Motility and morphodynamics of confined cells. Physical Review E , 101(2):022404, American Physical Society (APS), 2020.
[109]	Cucchi, A., Etchegaray, C., Meunier, N., Navoret, L. & Sabbagh, L. Cell migration in complex environments: chemotaxis and topographical obstacles. ESAIM: Proceedings and Surveys, 67:191-209, EDP Sciences, 2020.
[110]	Six, E., Guilloux, A., Denis, A., Lecoules, A., Magnani, A., Vilette, R., Male, F., Cagnard, N., Delville, M., Magrin, E., Caccavelli, L., Roudaut, C., Plantier, C., Sobrino, S., Gregg, J., Nobles, C.L., Everett, J.K., Hacein-Bey-Abina, S., Galy, A., Fischer, A., Thrasher, A.J., Andre, I., Cavazzana, M. & Bushman, F.D. Clonal tracking in gene therapy patients reveals a diversity of human hematopoietic differentiation programs. Blood, 135(15):1219-1231, American Society of Hematology, 2020.
[111]	Challamel, N., Lerbet, J., Darve, F. & Nicot, F. Buckling of granular systems with discrete and gradient elasticity Cosserat continua. Annals of Solid and Structural Mechanics, 12(1-2):7-22, Springer Berlin Heidelberg, 2020.
[112]	Lerbet, J., Challamel, N., Nicot, F. & Darve, F. Stability of Discrete Non-conservative Systems. , pages i-iiiElsevier, 2020.
[113]	Champion, M., Chiquet, J., Neuvial, P., Elati, M., Radvanyi, F. & Birmele, E.E. Identification of deregulated transcription factors involved in subtypes of cancers. In Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, pages 1-10, 2020.
[114]	Chiquet, J., Rigaill, G. & Sundqvist, M. aricode: Efficient Computations of Standard Clustering Comparison Measures. , 2020., ().
[115]	Bussy, S., Alaya, M.Z., Jannot, A.S. & Guilloux, A. Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics. , 2020., (working paper or preprint).
2019
[116]	Bussy, S., Guilloux, A., Gaiffas, S. & Jannot, A.S. C-mix: a high dimensional mixture model for censored durations, with applications to genetic data. Statistical Methods in Medical Research, 28(5):1523-1539, SAGE Publications, 2019.
[117]	Maire, V., Mahmood, F., Rigaill, G., Ye, M., Brisson, A., Nemati, F., Gentien, D., Tucker, G.C., Roman-Roman, S. & Dubois, T. LRP8 is overexpressed in estrogen-negative breast cancers and a potential target for these tumors. Cancer Medicine, 8(1):325-336, Wiley, 2019.
[118]	Robin, G., Ambroise, C. & Robin, S.S. Incomplete graphical model inference via latent tree aggregation. Statistical Modelling, 19(5):545-568, SAGE Publications, 2019.
[119]	Palomares, M.A., Dalmasso, C., Bonnet, E., Derbois, C., Brohard-Julien, S., Ambroise, C., Battail, C., Deleuze, J.F. & Olaso, R.E. Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples.. Scientific Reports, 9:1-12, Nature Publishing Group, 2019.
[120]	Morel, M., Bacry, E., Gaiffas, S., Guilloux, A. & Leroy, F. ConvSCCS: convolutional self-controlled case-seris model for lagged adverser event detection. Biostatistics, Oxford University Press (OUP), 2019.
[121]	Lannes, R., Rizzon, C. & Lerat, E. Does the Presence of Transposable Elements Impact the Epigenetic Environment of Human Duplicated Genes?. Genes, 10(3):249, MDPI, 2019.
[122]	Lemay, M.A., Torkamaneh, D., Rigaill, G., Boyle, B., Stec, A.O., Stupar, R.M. & Belzile, F. Screening populations for copy number variation using genotyping-by-sequencing: a proof of concept using soybean fast neutron mutants. BMC Genomics, 20:634, BioMed Central, 2019.
[123]	Vinet, M., Suresh, S., Maire, V., Monchecourt, C., Nemati, F., Lesage, L., Pierre, F., Ye, M., Lescure, A., Brisson, A., Meseure, D., Nicolas, A., Rigaill, G., Marangoni, E., del Nery, E., Roman-Roman, S. & Dubois, T. Protein arginine methyltransferase 5: A novel therapeutic target for triple-negative breast cancers. Cancer Medicine, 8(5):2414-2428, Wiley, 2019.
[124]	Alaya, M.Z., Bussy, S., Gaiffas, S. & Guilloux, A. Binarsity: a penalization for one-hot encoded features. Journal of Machine Learning Research, 20(118):1-34, Microtome Publishing, 2019.
[125]	Forst, E., Enjalbert, J., Allard, V., Ambroise, C., Krissaane, I., Mary Huard, T., Robin, S. & Goldringer, I. 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, 242:107571, Elsevier, 2019.
[126]	Bussy, S., Veil, R., Looten, V., Burgun, A., Gaiffas, S., Guilloux, A., Ranque, B. & Jannot, A.S. Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework. BMC Medical Research Methodology, 19(1):50, BioMed Central, 2019.
[127]	Fearnhead, P. & Rigaill, G. Changepoint detection in the presence of outliers. Journal of the American Statistical Association, 114(525):169-183, Taylor \& Francis, 2019.
[128]	Billat, V., Carbillet, T., Correa, M. & Pycke, J.R. Detecting the marathon asymmetry with a statistical signature. Physica A: Statistical Mechanics and its Applications, 515:240-247, Elsevier, 2019.
[129]	Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics. Algorithms for Molecular Biology, 14:22, BioMed Central, 2019.
[130]	Robin, S.S. & Ambroise, C. Applications in Genomics. In HANDBOOK OF MIXTURE ANALYSIS, CRC PRESS-TAYLOR \& FRANCIS GROUP, 2019.
[131]	Chiquet, J., Rigaill, G. & Sundqvist, M. A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer. In Gene Regulatory Networks, pages 143-160, 2019.
[132]	Hulot, A., Chiquet, J., Jaffrezic, F. & Rigaill, G. Fast tree aggregation for consensus hierarchical clustering: application to multi-omics data analysis. In Statistical Methods for Post-Genomic Data (SMPGD), 2019.
[133]	Lerbet, J., Challamel, N., Nicot, F. & Darve, F. Intrinsic Incremental Mechanics. In 4th International SEE Conference on Geometric Science of Information (GSI), 11712:51-54, 2019.
[134]	Enjalbert, J., Allard, V., Ambroise, C., Andrieu, B., Barot, S., Bentze, L., Blanc, E., Borg, J., Cantarel, A., Coleno, F., Pope de Vallavieille, C., Descoureaux, D., Dezette, D., Dubs, F., Feret, M., Forst, E., Fugeray-Scarbel, A., Galic, N., Gauffreteau, A., Gilet, J., Goldringer, I., Goussault, C., Hannachi, M., Hinsinger, P., Houivet, G., Hugoni, M., Jeuffroy, M.H., Kerbiriou, C., Labarthe, P., Lata, J., Leroux, X., Lecarpentier, C., Leconte, M., Lejars, L., Lemain, B., Lemarie, S., Leny, F., Le Viol, I., Lusley, P., Mary-Huard, T., Mirlicourtois, E., Montagnier, C., Niboyet, A., Omon, B., Perronne, R., Piaud, S., Pin, S., Cervek, C., Jouanne, S., Piaud, S., Poly, F., Pommier, T., Porcher, E., Saint-Jean, S., Salmon, S., Tropee, D., Vergnes, A., van Frank, G. & Vidal, T. An interdisciplinary approach to increase wheat within-field diversity and promote agro-ecosystem services. In European Conference on Crop Diversification, 2019.
[135]	Lefebvre, V., Maillot, G., Szadkowski, E., Caromel, B., Massire, A., Bachellez, A., Touhami, N., Rigaill, G., Chadoeuf, J. & Lamour, K. Impact of host partial resistance on the gene expression of Phytophthora capsici: consequences for pepper breeding. In 17. Eucarpia Meeting on Genetics and Breeding of Capsicum and Eggplant (2019), 2019.
[136]	Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. , 2019., (Poster).
[137]	Gautreau, G., Bazin, A., Planel, R., Gachet, M., Dubois, M., Burlot, L., Perrin, A., Touchon, M., Rocha, E., Ambroise, C., Matias, C., Medigue, C. & Vallenet, D. PanGBank: depicting microbial species diversity via PPanGGOLiN. , 2019., (Poster).
[138]	Chiquet, J., Rigaill, G. & Hulot, A. mergeTrees: Aggregating Trees. CRAN, 2019., ().
[139]	Garcia de La Torre, V.S., Majorel-Loulergue, C., Gonzalez, D.A., Soubigou-Taconnat, L., Rigaill, G., Pillon, Y., Barreau, L., Thomine, S., Fogliani, B., Burtet-Sarramegna, V. & Merlot, S. Wide cross-species RNA-Seq comparison reveals a highly conserved role for Ferroportins in nickel hyperaccumulation in plants. , 2019., (working paper or preprint).
[140]	Kamari, H., Huet, S. & Taupin, M.L. RKHSMetaMod: An R package to estimate the Hoeffding decomposition of an unknown function by solving RKHS ridge group sparse optimization problem. , 2019., (working paper or preprint).
[141]	Huet, S. & Taupin, M.L. Metamodel construction for sensitivity analysis. , 2019., (working paper or preprint).
[142]	Laso-Jadart, R., Sugier, K., Petit, E., Labadie, K., Peterlongo, P., Ambroise, C., Wincker, P., Jamet, J.L. & Madoui, M.A. Linking Allele-Specific Expression And Natural Selection In Wild Populations. , 2019., (working paper or preprint).
2018
[143]	Albert, E., Duboscq, R., Latreille, M., Santoni, S., Beukers, M., Bouchet, J.P., Bitton, F., Gricourt, J., Poncet, C., Gautier, V., Jimenez-Gomez, J., Rigaill, G. & Causse, M. Allele-specific expression and genetic determinants of transcriptomic variations in response to mild water deficit in tomato. The Plant Journal, 96(3):635-650, Wiley, 2018.
[144]	Jonchere, V., Marisa, L., Greene, M., Virouleau, A., Buhard, O., Bertrand, R., Svrcek, M., Cervera, P., Goloudina, A., Guillerm, E., Coulet, F., Landman, S., Ratovomanana, T., Job, S., Ayadi, M., Elarouci, N., Armenoult, L., Merabtene, F., Dumont, S., Parc, Y., Lefevre, J., Andre, T., Flejou, J.F., Guilloux, A., Collura, A., de Reynies, A. & Duval, A. Identification of Positively and Negatively Selected Driver Gene Mutations Associated With Colorectal Cancer With Microsatellite Instability Positive Selection Pressure Mutational Background Negative Selection Pressure Mutational Frequency Microsatellite length (bp). Cellular and Molecular Gastroenterology and Hepatology, 6(3):277-300, Philadelphia, PA : American Gastroenterological Association, [2015]-, 2018.
[145]	Celisse, A., Marot, G., Male, P.J. & Rigaill, G. New efficient algorithms for multiple change-point detection with reproducing kernels. Computational Statistics and Data Analysis, 128:200-220, Elsevier, 2018.
[146]	Guinot, F., Szafranski, M., Ambroise, C. & Samson, F. Learning the optimal scale for GWAS through hierarchical SNP aggregation. BMC Bioinformatics, 19, BioMed Central, 2018.
[147]	Guinot, F., Szafranski, M., Ambroise, C. & Samson, F. Learning the optimal scale for GWAS through hierarchical SNP aggregation. BMC Bioinformatics, 19(1):459, BioMed Central, 2018.
[148]	Sadacca, B., Hamy, A.S., Laurent, C., Gestraud, P., Bonsang-Kitzis, H., Pinheiro, A., Abecassis, J., Neuvial, P. & Reyal, F. New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels (vol 7, 15126, 2016). Scientific Reports, 8, Nature Publishing Group, 2018., (Author Correction: New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels).
[149]	Lloyd, A., Blary, A., Charif, D., Charpentier, C., Tran, J., Balzergue, S., Delannoy, E., Rigaill, G. & Jenczewski, E. Homoeologous exchanges cause extensive dosage-dependent gene expression changes in an allopolyploid crop. New Phytologist, 217(1):367-377, Wiley, 2018.
[150]	Billat, V., Brunel, N.J.B., Carbillet, T., Labbe, S. & Samson, A. Humans are able to self-paced constant running accelerations until exhaustion. Physica A: Statistical Mechanics and its Applications, 506:290-304, Elsevier, 2018.
[151]	Sow, M.D., Allona, I., Ambroise, C., Conde, D., Fichot, R.R., Gribkova, S., Jorge, V., Le Provost, G., Paques, L., Plomion, C., Salse, J., Sanchez Rodriguez, L., Segura, V., Tost, J. & Maury, S. Epigenetics in Forest Trees: State of the Art and Potential Implications for Breeding and Management in a Context of Climate Change. In Plant Epigenetics Coming of Age for Breeding Applications, 88:454 p., Academic press, Elsevier, 2018., (Chapitre 12).
[152]	Malbert, B., Rigaill, G., Brunaud, V., Lurin, C. & Delannoy, E. Bioinformatic analysis of chloroplast gene expression and RNA posttranscriptional maturations using RNA Sequencing. In Plastids, 1829:16, Humana Press Inc., 2018.
[153]	Brunel, N., Goujot, D., Labarthe, S. & Laroche, B. Parameter estimation for dynamical systems using an FDA approach. In 11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018), 2018.
[154]	Gautreau, G., Ambroise, C., Matias, C., Perrin, A., Planel, R., Touchon, M., Medigue, C., Rocha, E., Cruveiller, S. & Vallenet, D. PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome Graph. In JOBIM 2018 Journées Ouvertes Biologie, Informatique et Mathématiques, 2018.
[155]	Hulot, A., Chiquet, J., Jaffrezic, F. & Rigaill, G. Fused-ANOVA : une méthode de clustering en grande dimension. In 50èmes Journées de Statistique, 2018.
[156]	Gautreau, G., Ambroise, C., Matias, C., Perrin, A., Planel, R., Touchon, M., Medigue, C., Rocha, E., Cruveiller, S. & Vallenet, D. PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome Graph. In Genome Informatics 2018, 2018.
[157]	Ambroise, C., Dehman, A., Koskas, M., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. In Journée Régionale de Bioinformatique et Biostatistique, Génopole Toulouse, 2018.
[158]	Frouin, A., Le Flocch, E. & Ambroise, C. Quantify Genomic Heritability Through a Prediction Measure. In 46th European Mathematical Genetics Meeting (EMGM), 83-1:2, Kanger - basel - Switzerland, 2018.
[159]	Sundqvist, M., de Koning, L., Rigaill, G., Dubois, T. & Chiquet, J. PO-435 Proteomic classification of triple negative breast cancer. , 3:A402, 2018., (Poster).
[160]	Virouleau, A., Guilloux, A., Gaiffas, S. & Bogdan, M. HIGH-DIMENSIONAL ROBUST REGRESSION AND OUTLIERS DETECTION WITH SLOPE. , 2018., (working paper or preprint).
[161]	Alaya, M.Z., Lemler, S., Guilloux, A. & Allart, T. High-dimensional time-varying Aalen and Cox models. , 2018., (working paper or preprint).
2017
[162]	Sadacca, B. Pharmacogenomic and High-Throughput Data Analysis to Overcome Triple Negative Breast Cancers Drug Resistance. , 2017. (Theses)
[163]	Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Beyond support in two-stage variable selection. Statistics and Computing, 27(1):169-179, Springer Verlag (Germany), 2017.
[164]	Huet, S. & Taupin, M.L. Metamodel construction for sensitivity analysis. ESAIM: Proceedings and Surveys, 60-2017:27-69, EDP Sciences, 2017.
[165]	Bayad, A., Dossavi-Yovo, A., Filipin, A. & Togbe, A. On the extensibility of the D(4)-triple \k -2, k + 2, 4k\ over Gaussian integers. Notes on Number Theory and Discrete Mathematics, 23(3), Academic Publishing House of the Bulgarian Academy of Sciences, 2017.
[166]	Stanislas, V., Dalmasso, C. & Ambroise, C. Eigen-Epistasis for detecting Gene-Gene interactions. BMC Bioinformatics, 18:54, BioMed Central, 2017.
[167]	Sadacca, B., Hamy-Petit, A.S., Laurent, C., Gestraud, P., Bonsang-Kitzis, H., Pinheiro, A., Abecassis, J., Neuvial, P. & Reyal, F. New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels. Scientific Reports, 7(1):15126, Nature Publishing Group, 2017., (Author Correction : Sadacca, B., Hamy, A.-S., Laurent, C., Gestraud, P., Bonsang-Kitzis, H., Pinheiro, A., Abecassis, J., Neuvial, P., Reyal, F. (2018). New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels (vol 7, 15126, 2016). Scientific Reports, 8, 1-1 https://www.nature.com/articles/s41598-018-36812-3 UT=WOS000453222300001).
[168]	Stanislas, V., Dalmasso, C. & Ambroise, C. Eigen-Epistasis for detecting gene-gene interactions. BMC Bioinformatics, 18:np, BioMed Central, 2017.
[169]	Biessy, G. Continuous-time semi-markov inference of biometric laws associated with a long-term care insurance portfolio. ASTIN Bulletin, 47(2):527-561, Cambridge University Press (CUP), 2017.
[170]	Huet, S. & Taupin, M.L. Metamodel construction for sensitivity analysis. ESAIM: Proceedings and Surveys, 60:27-69, EDP Sciences, 2017.
[171]	Allart, T., Levieux, G., Pierfitte, M., Guilloux, A. & Natkin, S. Difficulty influence on motivation over time in video games using survival analysis. In FDG '17: Proceedings of the 12th International Conference on the Foundations of Digital Games, 6, ACM Press, 2017.
[172]	Tabouy, T., Barbillon, P. & Chiquet, J. Variational Inference for Stochastic Block Models from Sampled Data. , 2017., (working paper or preprint).
2016
[173]	Biessy, G. Modélisation semi-markovienne de la perte d'autonomie chez les personnes âgées : application à l'assurance dépendance. , 2016. (Theses)
[174]	Pierre-Jean, M. Development of statistical methods for DNA copy number analysis in cancerology.. , 2016. (Theses)
[175]	Chiquet, J., Grandvalet, Y. & Rigaill, G. On coding effects in regularized categorical regression. Statistical Modelling, 16(3):228-237, SAGE Publications, 2016.
[176]	Koumakis, E., Bouaziz, M., Dieude, P., Cauvet, A., Ruiz, B., Airo, P., Cusi, D., Matucci-Cerinic, M., Salvi, E., Cuome, G., Hachulla, E., Diot, E., Caramaschi, P., Riccieri, V., Avouac, J. & Allanore, Y. A candidate gene study identifies a haplotype of CD2 as novel susceptibility factor for systemic sclerosis. Clinical and experimental rheumatology, 34(5):S43-S48, Clinical and Experimental Rheumatology Sas, 2016.
[177]	Brouard, C., Szafranski, M. & d'Alche-Buc, F. Input output Kernel regression : supervised and semi-supervised structured output prediction with operator-valued kernels. Journal of Machine Learning Research, 17:np, Microtome Publishing, 2016.
[178]	Carapito, R., Jung, N., Kwemou, M., Untrau, M., Michel, S., Pichot, A., Giacometti, G., Macquin, C., Ilias, W., Morlon, A., Kotova, I., Apostolova, P., Schmitt-Graeff, A., Cesbron, A., Gagne, K., Oudshoorn, M., van Der Holt, B., Labalette, M., Spierings, E., Picard, C., Loiseau, P., Tamouza, R., Toubert, A., Parissiadis, A., Dubois, V., Lafarge, X., Maumy-Bertrand, M., Bertrand, F., Vago, L., Ciceri, F., Paillard, C., Querol, S., Sierra, J., Fleischhauer, K., Nagler, A., Labopin, M., Inoko, H., von Dem Borne, P., Kuball, J.H.E., Ota, M., Katsuyama, Y., Michallet, M., Lioure, B., Peffault de Latour, R., Blaise, D., Cornelissen, J.J., Yakoub-Agha, I., Claas, F., Moreau, P., Milpied, N., Charron, D., Mohty, M., Zeiser, R., Socie, G. & Bahram, S. Matching for the non-conventional MHC-I MICA gene significantly reduces the incidence of acute and chronic GVHD. Blood, American Society of Hematology, 2016.
[179]	Ghirelli, C., Sadacca, B., Reyal, F., Zollinger, R., Michea, P., Sirven, P., Pattarini, L., Martinez-Cingolani, C., Guillot-Delost, M., Nicolas, A., Scholer-Dahirel, A. & Soumelis, V. No evidence for TSLP pathway activity in human breast cancer. OncoImmunology, 5(8):1-32, Taylor \& Francis, 2016.
[180]	Guilloux, A., Lemler, S. & Taupin, M.L. Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates. Journal of Multivariate Analysis, 148:141-159, Elsevier, 2016.
[181]	Kwemou, M. Non-asymptotic oracle inequalities for the Lasso and Group Lasso in high dimensional logistic model. ESAIM: Probability and Statistics, 20:309-331, EDP Sciences, 2016.
[182]	Bonsang-Kitzis, H., Sadacca, B., Hamy-Petit, A.S., Moarii, M., Pinheiro, A., Laurent, C. & Reyal, F. Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis. OncoImmunology, 5(1), Taylor \& Francis, 2016.
[183]	Dumas, A.S., Taconnat, L., Barbas, E., Rigaill, G., Catrice, O., Bernard, D., Benamar, A., Macherel, D., El Amrani, A. & Berthome, R. Unraveling the early molecular and physiological mechanisms involved in response to phenanthrene exposure. BMC Genomics, 17(1):818, BioMed Central, 2016.
[184]	Vacher, C., Tamaddoni-Nezhad, A., Kamenova, S., Dubois Peyrard, N., Moalic, Y., Sabbadin, R., Schwaller, L., Chiquet, J., Alex Smith, M., Vallance, J., Fievet, V., Jakuschkin, B. & Bohan, D. Learning ecological networks from next-generation sequencing data. In Ecosystem Services: From Biodiversity to Society, Part 2, 54:np, 2016.
[185]	Aubouin, N., Coblence, E., Dalmasso, C. & Kletz, F. Transformation des organisations culturelles et des métiers : quelles formes de reconnaissance ? . In STP\&A, 2016.
[186]	Biessy, G. A semi-Markov model with pathologies for Long-Term Care Insurance. , 2016., (working paper or preprint).
[187]	Celisse, A., Marot, G., Pierre-Jean, M. & Rigaill, G. New efficient algorithms for multiple change-point detection with kernels. , 2016., (working paper or preprint).
2015
[188]	Chiquet, J. Contributions to Sparse Methods for Complex Data Analysis. , 2015.
[189]	Dehman, A. Spatial Clustering of Linkage Disequilibrium blocks for Genome-Wide Association Studies. , 2015. (Theses)
[190]	Chevalier, E., Vath, V.L., Roch, A. & Scotti, S. Optimal exit strategies for investment projects. Journal of Mathematical Analysis and Applications, 425(2):666-694, Elsevier, 2015.
[191]	Picchetti, T., Chiquet, J., Elati, M., Neuvial, P., Nicolle, R. & Birmele, E.E. A model for gene deregulation detection using expression data. BMC Systems Biology, 9, BioMed Central, 2015.
[192]	Dehman, A., Ambroise, C. & Neuvial, P. Performance of a blockwise approach in variable selection using linkage disequilibrium information. BMC Bioinformatics, 16:14, BioMed Central, 2015.
[193]	Guilloux, A., Lemler, S. & Taupin, M.L. Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates. Journal of Statistical Planning and Inference, 171:38-62, Elsevier, 2015.
[194]	Grbac, Z., Crepey, S., Ngor, N. & Skovmand, D. A Lévy HJM multiple-curve model with application to CVA computation. Quantitative Finance, 15(3):401-419, Taylor \& Francis (Routledge), 2015.
[195]	Dalmasso, C., Carpentier, W., Guettier, C., Camilleri-Broet, S., Vendramini Borelli, W., Campos dos Santos, C.R., Castaing, D., Duclos-Vallee, J.C. & Broet, P. Patterns of chromosomal copy-number alterations in intrahepatic cholangiocarcinoma. BMC Cancer, 15:126, BioMed Central, 2015.
[196]	Pierre-Jean, M., Rigaill, G. & Neuvial, P. Performance evaluation of DNA copy number segmentation methods. Briefings in Bioinformatics, 16(4):600-615, Oxford University Press (OUP), 2015.
[197]	Latouche, P., Mattei, P.A., Bouveyron, C. & Chiquet, J. Combining a Relaxed EM Algorithm with Occam's Razor for Bayesian Variable Selection in High-Dimensional Regression. Journal of Multivariate Analysis, 146:177-190, Elsevier, 2015.
[198]	Chambaz, A. & Neuvial, P. tmle.npvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation: Fig. 1.. Bioinformatics, 31(18):3054-6, Oxford University Press (OUP), 2015.
[199]	Latouche, P., Birmele, E. & Ambroise, C. Handbook of Mixed Membership Models and Their Applications. , pages 547-568Chapman and Hall, 2015.
[200]	Ambroise, C., Chiquet, J. & Szafranski, M. A greedy great approach to learn with complementary structured datasets. In Greed Is Great ICML Workshop, 2015.
[201]	Franchinard, M.L., Sapet, F., Derozier, S.S., Samson, F.F. & Gibrat, J.F. View and synchronize several genotypes with IGV. In JOBIM 2015 - Journées Ouvertes Biologie Informatique Mathématiques, pages 1 p., 2015., (Événement(s) lié(s) : - AG France Genomique; Paris (FRA) - (2015-11-24 - 2015-11-25)).
[202]	Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Significance testing for variable selection in high-dimension. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pages 1-8, IEEE, 2015.
[203]	Chiquet, J., Szafranski, M. & Ambroise, C. A greedy great approach to learn with complementary structured datasets. , pages np, 2015., (Poster - HAL Id : hal-01246419, version 1 HAL Id : hal-01246419, version 1).
[204]	Daccord, N., Corel, E., Correia, D., Louis, A., Debat, H., Daric, V., Nadal, M., Devauchelle, C. & Samson, F.F. TopoIBase: a comprehensive database dedicated to type IA DNA-topoisomerases. In JOBIM 2015 - Journées Ouvertes Biologie Informatique Mathématiques, 2015.
[205]	Guilloux, A., Lemler, S. & Taupin, M. Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates. , 2015.
[206]	Pierre-Jean, M. Non parametric DNA copy number segmentation using kernels. , 2015., (SMPGD'2015).
[207]	Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Beyond Support in Two-Stage Variable Selection. , 2015., (working paper or preprint).
[208]	Brouard, C., d'Alche-Buc, F. & Szafranski, M. Input Output Kernel Regression. , 2015., (working paper or preprint).
[209]	Kwemou, M., Taupin, M.L. & Tocquet, A.S. MODEL SELECTION IN LOGISTIC REGRESSION. , 2015., (working paper or preprint).
[210]	Biessy, G. Continuous time semi-Markov inference of biometric laws associated with a Long-Term Care Insurance portfolio. , 2015., (working paper or preprint).
[211]	Guilloux, A., Lemler, S. & Taupin, M.L. Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates. , 2015., (working paper or preprint).
2014
[212]	Kwemou, M. Réduction de dimension en régression logistique, application aux données ACTU-PALU. , 2014. (Theses)
[213]	Lemler, S. Estimation for counting processes with high-dimensional covariates. , 2014. (Theses)
[214]	Lemler, S. Estimation for counting processes with high-dimensional covariates. , 2014. (Theses)
[215]	Latouche, P., Birmele, E. & Ambroise, C. Model Selection in Overlapping Stochastic Block Models. Electronic Journal of Statistics, 8:762-794, 2014.
[216]	Dedecker, J., Samson, A. & Taupin, M.L. Estimation in autoregressive model with measurement error. ESAIM Probab. \& Stat., 18():277-307, 2014., (http://dx.doi.org/10.1051/ps/2013037).
[217]	Lemler, S. Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model. Accepted in Les Annales de l'Institut Henri Poincaré, 00(?), 2014.
[218]	Pierre-Jean, M., Rigaill, G. & Neuvial, P. Performance evaluation of DNA copy number segmentation methods. Briefings in Bioinformatics, 2014.
[219]	crepey, S., Grbac, Z., Ngor, N. & Skovmand, D. A Lévy HJM multiple-curve model with application to CVA computation. Quantitative Finance, 2014., (Forthcoming (DOI:10.1080/14697688.2014.942232)).
[220]	Kim, D., Bayad, A. & Park, J. Euler polynomials and combinatoric convolution sums of divisor functions with even indices. Abstr. Appl. Anal., pages Art. ID 289187, 6, 2014.
[221]	Kim, D., Bayad, A. & Park, J. Euler Polynomials and Combinatoric Convolution Sums of Divisor Functions with Even Indices. Abstract and Applied Analysis, 2014:1-6, Hindawi Publishing Corporation, 2014.
[222]	Dedecker, J., Samson, A. & Taupin, M.L. Estimation in autoregressive model with measurement error. ESAIM: Probability and Statistics, 18:277-307, EDP Sciences, 2014.
[223]	Hahn, G., Bujan, A.F., Fregnac, Y., Aertsen, A., Kumar, A. & Brunel, N. Communication through Resonance in Spiking Neuronal Networks. PLoS Computational Biology, 10(8):e1003811, PLOS, 2014.
[224]	Chalhoub, B., Denoeud, F., Liu, S., Parkin, I.A.P., Tang, H., Wang, X., Chiquet, J., Belcram, H., Tong, C., Samans, B., Correa, M., da Silva, C., Just, J., Falentin, C., Koh, C.S., Le Clainche, I., Bernard, M., Bento, P., Noel, B., Labadie, K., Alberti, A.A., Charles, M., Arnaud, D., Guo, H., Daviaud, C., Alamery, S., Jabbari, K., Zhao, M., Edger, P.P., Chelaifa-Ammari, H., Tack, D., Lassalle, G., Mestiri, I., Schnel, N., Le Paslier, M.C., Fan, C., Fan, G., Renault, V., Bayer, P.E., Golicz, A.A., Manoli, S., Lee, T.H., Dinh Thi, V.H., Chalabi, S., Hu, Q., Tollenaere, R., Lu, Y., Battail, C., Shen, J., Sidebottom, C.H.D., Wang, X., Canaguier, A., Chauveau, A., Berard, A., Deniot, G., Guan, M., Liu, Z., Sun, F., Lim, Y.P., Lyons, E., Town, C.D., Bancroft, I., Wang, X., Meng, J., Ma, J., Pires, J.C., King, G.J., Brunel, D., Delourme, R., Renard, M., Aury, J.M., Adams, K.L., Wincker, P., Batley, J., Snowdon, R.J., Tost, J., Edwards, D., Zhou, Y., Hua, W., Sharpe, A.G., Paterson, A.H. & Guan, C. Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome. Science, 345(6199):950-953, American Association for the Advancement of Science (AAAS), 2014.
[225]	Matias, C. & Robin, S. Modeling heterogeneity in random graphs through latent space models: a selective review. ESAIM: Proceedings, 47:55-74, EDP Sciences, 2014.
[226]	Jeanmougin, M., Charbonnier, C., Guedj, M. & Chiquet, J. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. , ():xxx, Oxford University Press, 2014.
[227]	Chazottes, J., Cuny, C., Dedecker, J., Fan, X. & Lemler, S. Limit theorems and inequalities via martingale methods. In ESAIM: Proceedings, 44():177-196, , 2014.
[228]	Laloe, D., Jaffrezic, F., Chiquet, J. & Gaultier, M. FLPCA: a fused-LASSO PCA-based approach to identify footprints of selection in differentiated populations from dense to SNP data: applications to human and cattle data. In Proceedings of the International Biometric Conference, Florence, Italy, ():, , 2014.
[229]	Kwemou, M., Taupin, M.L., Dos Santos, S. & Le Hesran, J.Y. Variables Selection Strategies in large Socio-epidemiological Surveys. Identification of households at risk of having febrile episode in Dakar, Senegal. , 2014.
[230]	Matias, C. & Robin, S. Modeling heterogeneity in random graphs: a selective review. , 2014.
[231]	Clairon, Q. & Brunel, N.J.B. State and Parameter Estimation of Partially Observed Linear Ordinary Differential Equations with Deterministic Optimal Control. , 2014., (working paper or preprint).
[232]	Brunel, N.J.B. & Clairon, Q. A Tracking Approach to Parameter Estimation in Linear Ordinary Differential Equations. , 2014., (working paper or preprint).
2013
[233]	Brito, I., Hupe, P., Neuvial, P. & Barillot, E. Stability-based comparison of class discovery methods for array-CGH profiles. PLoS One, 8(12):e81458, 2013.
[234]	Chelaifa, H., Chague, V., Chalabi, S., Mestiri, I., Arnaud, D., Deffains, D., Lu, Y., Belcram, H., Huteau, V., Chiquet, J., Coriton, O., Just, J., Jahier, J. & Chaloub, B. Prevalence of gene expression additivity in genetically stable wheat allohexaploids. New Phytologist, 197(3):730-736, 2013.
[235]	Neuvial, P. Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators. Journal of Machine Learning Research, 14():1423\−1459, 2013.
[236]	crepey, S., Gerboud, R., Grbac, Z. & Ngor, N. Counterparty risk and funding: the four wings of the TVA. Int. J. Theor. Appl. Finance, 16(2):1350006-31, 2013.
[237]	Chevalier, E., Ly Vath, V. & Scotti, S. An optimal dividend and investment control problem under debt constraints. SIAM J. Financial Math., 4(1):297-326, 2013.
[238]	Kharroubi, I., Lim, T. & Ngoupeyou, A. Mean-variance hedging on uncertain time horizon in a market with a jump. Appl. Math. Optim., 68(3):413-444, 2013.
[239]	Chevalier, E., Vath, V.L. & Scotti, S. An Optimal Dividend and Investment Control Problem under Debt Constraints. SIAM Journal on Financial Mathematics, 4(1):297-326, Society for Industrial and Applied Mathematics , 2013.
[240]	Chiquet, J. & Limnios, N. Stochastic Reliability and Maintenance Modeling. , 9():, Springer, 2013.
[241]	Chiquet, J., Mary-Huard, T. & Robin, S. Multi-trait genomic selection via multivariate regression with structured regularization. In Proceedings of the MLCB NIPS'13 workshop, ():, , 2013.
[242]	Dehman, A., Ambroise, C. & Neuvial, P. Incorporating linkage disequilibrium blocks in Genome-Wide Association Studies. In JOBIM proceeding 2013, ():, , 2013.
[243]	Gutierrez, P., Rigaill, G. & Chiquet, J. A fast homotopy algorithm for a large class of weighted classification problems. In Proceedings of the MLCB NIPS'13 workshop, South lake Thao, ():, , 2013.
[244]	Neuvial, P. Vers une médecine personnalisée grâce à la recherche en génomique. , 2013., (Variances Vol. 48 pp 31-33).
[245]	crepey, S., Z., G., Ngor, N. & Skovmand, D. A Lévy HJM multiple-curve model with application to CVA computation. , 2013.
[246]	Dehman, A. Incorporating Linkage Disequilibrium Blocks in Genome-Wide Association Studies. , 2013.
[247]	Pierre-Jean, M. Joint segmentation of total DNA copy number and allelic ratios in cancer samples. , 2013., (SMPGD'2013).
[248]	Pierre-Jean, M. Change-point detection with kernel methods : application to DNA copy number signals. , 2013., (45e Journées de Statistiques de la SFDS , Toulouse).
[249]	Pierre-Jean, M. Package R jointSeg : Segmentation de données génomiques en cancérologie. , 2013., (Rencontres R , Lyon).
2012
[250]	Jeanmougin, M. Statistical methods for robust analysis of transcriptome data by integration of biological knowledge.. , 2012. (Theses)
[251]	Acuna, V., Birmele, E., Cottret, L., Crescenzi, P., Jourdan, F., Lacroix, V., Marchetti-Spaccamela, A., Marinov, A., Vieira Milreu, P., Sagot, M.F. & Stougie, L. Telling Stories: Enumerating maximal directed acyclic graphs with a constrained set of sources and targets. Theoretical Computer Science, 457(2):1-9, 2012.
[252]	Ambroise, C. & Matias, C. New consistent and asymptotically normal parameter estimates for random graph mixture models. Journal of the Royal Statistical Society: Series B, 74(1):3-35, 2012.
[253]	Bouaziz, M., Paccard, C., Guedj, M. & Ambroise, C. SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies. PloS One, 7(10):e45685, 2012.
[254]	Chambaz, A., Neuvial, P. & van der Laan, M. Estimation of a non-parametric variable importance measure of a continuous exposure. Electronic Journal of Statistics, 6():1059-1099, 2012.
[255]	Chiquet, J., Grandvalet, Y. & Charbonnier, C. Sparsity with sign-coherent groups of variables via the cooperative-Lasso. The Annals of Applied Statistics, 6(2):795-830, 2012.
[256]	Didier, G., Corel, E., Laprevotte, I., Grossmann, A. & Devauchelle, C. Variable length local decoding and alignment-free sequence comparison. Theoretical Computer Science, 462():1-11, 2012.
[257]	Dillies, M., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M. & Servant, N. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics, xx():, 2012.
[258]	Guergnon, J., Dalmasso, C., Broet, P., Meyer, L., Westrop, S., Imami, N., Vicenzi, E., Morsica, G., Tinelli, M., Poma, B., Goujard, C., Potard, V., Gotch, F., Casoli, C., Cossarizza, A. & others, O. Single Nucleotide Polymorphism-defined Class-I and Class-III MHC genetic subregions contribute to natural long-term non progression in HIV infection. Journal of Infectious Diseases, 205(5):718-24, 2012.
[259]	Jacob, L., Neuvial, P. & Dudoit, S. More Power via Graph-Structured Tests for Differential Expression of Gene Networks. Annals of Applied Statistics, 6(2):561-600, 2012.
[260]	Latouche, P., Birmele, E. & Ambroise, C. Variational Bayesian Inference and Complexity Control for Stochastic Block Models. Statistical Modelling, 12(1):93-115, 2012.
[261]	Neuvial, P. & Roquain, E. On False Discovery Rate thresholding for classification under sparsity. Annals of Statistics, 40(5):2572-2600, 2012.
[262]	Ortiz-Estevez, M., Aramburu, A., Bengtsson, H., Neuvial, P. & Rubio, A. CalMaTe: A Method and Software to Improve Allele-Specific Copy Number of SNP Arrays for Downstream Segmentation. Bioinformatics, 28(13):1793-1794, 2012.
[263]	Prum, B. Chaînes de Markov et absorption ; application à l'algorithme de Fu en génomique. J. Société Française de Statistique, 152 n°2():37-51, 2012.
[264]	Bouaziz, M., Jeanmougin, M. & Guedj, M. Multiple-testing in large-scale genetic studies. , ():, Bonin A, Pompanon F eds, Methods in Molecular Biology Series, Humana Press., 2012.
[265]	Jeanmougin, M. Improving gene signatures by the identification of differentially expressed modules in molecular networks : a local-score approach.. In JOBIM, ():103-111, , 2012., (Ambroise, Christophe and Bouaziz, M. and Guedj, M.).
[266]	Brunel, N., Clairon, Q. & d'Alche Buc, F. Parametric Estimation of Ordinary Differential Equations with Orthogonality Conditions. , 2012.
[267]	Grandvalet, Y., Chiquet, J. & Ambroise, C. Sparsity by Worst-Case Quadratic Penalties. , 2012.
[268]	Kwemou, M. Non-asymptotic Oracle Inequalities for the Lasso and Group Lasso in high dimensional logistic model. , 2012.
[269]	Chevalier, E. & Ngoupeyou, A. American put option on a defaultable asset and option based portfolio insurance. , 2012.
[270]	Lim, T., y, , Sahut, J.M. & Scotti, S. Bid-ask spread modelling, a perturbation approach. , 2012.
[271]	Pierre-Jean, M. Segmentation de données génomiques en cancérologie. , 2012., (Journée annuelle du groupe Biopharmacie et Santé de la SFDS).
2011
[272]	Devauchelle, C. De l'art de résumer pour tenter de comprendre en génomique évolutive. , 2011.
[273]	Bouaziz, M., Ambroise, C. & Guedj, M. Accounting for Population Stratification in Practice: a Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies. PLOS one, 6(12):, 2011.
[274]	Broet, P., Dalmasso, C., Tan, E., Alifano, M., Zhang, S., Wu, J., Lee, M., Regnard, J., Lim, W., Koong, H., Agasthian, T., Miller, L., Camilleri-Broet, S. & Tan, P. Genomic Profiles Specific to Patient Ethnicity in Lung Adenocarcinoma. Clinical Cancer Research, 17(11):3542-50, 2011.
[275]	Chiquet, J., Grandvalet, Y. & Ambroise, C. Inferring Multiple Graphical Structures. Statistics and Computing, 21(4):537-553, 2011.
[276]	Dalmasso, C. & Broet, P. Detection of chromosomal abnormalities using high resolution arrays in clinical cancer research. Journal of Biomedical Informatics, (doi:10.1016/j.jbi.2011.06.003):, 2011.
[277]	Jeanmougin, M., Guedj, M. & Ambroise, C. Defining a robust biological prior from Pathway Analysis to drive Network Inference.. J-SFdS, 152(2):, 2011.
[278]	Latouche, P., Birmele, E. & Ambroise, C. Overlapping Stochastic Block Models with Application to the French Political Blogosphere. Annals of Applied Statistics, 5(1):309-336, 2011.
[279]	Olshen, A., Bengtsson, H., Neuvial, P., Spellman, P., Olshen, R. & Seshan, V. Parent-specific copy number in paired tumor-normal studies using circular binary segmentation. Bioinformatics, 27(15):2038-2046, 2011.
[280]	Neuvial, P., Bengtsson, H. & Speed, T. Handbook of Statistical Bioinformatics. , ():225-255, Springer, 2011.
[281]	Chiquet, J., Grandvalet, Y. & Charbonnier, C. Sparsity with sign-coherent groups of variables via the cooperative-Lasso. In Proceedings of SPARS'11, Edimburgh, ():, , 2011.
[282]	Chiquet, J. Réseaux biologiques. , 2011., (La gazette des mathématiciens No. 130 pp. 76--82).
[283]	Neuvial, P. Tests multiples en génomique. , 2011., (La gazette des mathématiciens No. 130 pp. 71-76).
[284]	Jeanmougin, M. Network inference using pathway analysis results as a prior information.. , 2011., (SMPGD'11).
2010
[285]	Charbonnier, C., Chiquet, J. & Ambroise, C. Weighted-Lasso for Structured Network Inference from Time Course Data. Statistical Applications in Genetics and Molecular Biology, 9(1):, 2010.
[286]	Corel, E., Pitschi, F., Laprevotte, I., Grasseau, G., Didier, G. & Devauchelle, C. MS4 - Multi-Scale Selector of Sequence Signatures: An alignment-free method for classification of biological sequences. BMC Bioinformatics, 11(406):, 2010., (doi:10.1186/1471-2105-11-406).
[287]	Jeanmougin, M., de Reynies, A., Marisa, L., Paccard, C., Nuel, G. & Guedj, M. Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies. PLoS ONE, 5(9):e12336, 2010.
[288]	Pitschi, F., Devauchelle, C. & Corel, E. Automatic detection of anchor points for multiple sequence alignment. BMC Bioinformatics, 11(445):, 2010.
[289]	Zanghi, H., Picard, F., Miele, V. & Ambroise, C. Strategies for Online Inference of Network Mixture. Annals of Applied Statistics, 4(2):687-714, 2010.
[290]	Zanghi, H., Volant, S. & Ambroise, C. Clustering based on random graph model embedding vertex features. Pattern Recognition Letters, 31(9):830-836, 2010.
[291]	, (eds). La démarche statistique. , (), Cépaduès, 2010.
[292]	Charbonnier, C., Chiquet, J. & Ambroise, C. Weighted-Lasso for Structured Network Inference for Time-Course data. In JOBIM'10, Montpellier, 2010.
[293]	Chiquet, J., Grandvalet, Y. & Ambroise, C. Inferring Multiple Graphical Structures. In Workshop MODGRAPHII, JOBIM'10, Montpellier, 2010.
[294]	Grandvalet, Y., Chiquet, J. & Ambroise, C. Inferring Multiple Regulation Networks. In Proceedings of the MLCB NIPS'10 Workshop, Vancouver, 2010.
[295]	Grandvalet, Y., Chiquet, J. & Ambroise, C. Inférence jointe de la structure de modèles graphiques gaussiens. In actes de CAp'10, Clermont-Ferrand, 2010.
[296]	Ngoupeyou, A. Optimisation des portefeuilles d'actifs soumis au risque de défaut. , 2010.
[297]	Jeanblanc, M., Matoussi, A. & Ngoupeyou, A. Robust utility maximization in a discontinuous filtration. , 2010.
2009
[298]	Ambroise, C., Chiquet, J. & Matias, C. Inferring sparse Gaussian graphical models with latent structure. Electronic Journal of Statistics, 3():205-238, 2009.
[299]	Chiquet, J., Smith, A., Grasseau, G., Matias, C. & Ambroise, C. SIMoNe: Statistical Inference for MOdular NEtworks. Bioinformatics, 25(3):417-418, 2009.
[300]	Durot, C., Lebarbier, E. & Tocquet, A.S. Estimating the joint distribution of independent categorical variables via model selection. Bernoulli, 15(2):475-507, 2009.
[301]	Guedj, M., Celisse, A., Robin, S. & Nuel, G. kerfdr: A semi-parametric kernel-based approach to local FDR estimations.. BMC Bioinformatics, 10():84+, 2009.
[302]	Oudot, T., Lesueur, F., Guedj, M., de Cid, R., McGinn, S., Heath, S., Foglio, M., Prum, B., Lathrop, M., Prud'homme, J. & Fischer, J. An association study of 22 candidate genes in psoriasis families reveals shared genetic factors with other autoimmune and skin disorders. J Invest Dermatol., 129(11):2637-45, 2009.
[303]	Picard, F., Miele, V., Daudin, J.J., Cottret, L. & Robin, S. Deciphering the connectivity structure of biological networks using MixNet. BMC Bioinformatics, 10(Suppl 6):S17, 2009.
[304]	Tian, Z., Rizzon, C., Du, J., Zhu, L., Bennetzen, J., Gaut, B., Jackson, S. & Ma, J. Do genetic recombination and gene density shape the pattern of DNA elimination in rice LTR-retrotransposons?. Genome Res., 19(12):2221-30, 2009.
[305]	Ambroise, C. & Dang, M. Data Analysis. , ():289-318, Wiley, 2009.
[306]	Latouche, P., Birmele, E. & Ambroise, C. Advances in Data Analysis, Data Handling and Business Intelligence. , ():229-239, springer, 2009.
[307]	Chiquet, J., Charbonnier, C. & Ambroise, C. SIMoNe : Statistical Inference of Modular Network. In Workshop MODGRAPH, JOBIM'09, Nantes, 2009.
[308]	Latouche, P., Birmele, E. & Ambroise, C. Uncovering overlapping clusters in biological networks. In Journées ouvertes en biologie, informatique et mathématiques (Jobim). Nantes, 2009.
2008
[309]	Birmele, E., Elati, M., Rouveirol, C. & Ambroise, C. Identification of functional modules based on transcriptional regulation structure. BMC Proceedings, 2((Suppl 4):S4):, 2008.
[310]	Daudin, J.J., Picard, F. & Robin, S. A mixture model for random graphs. Statist. Comput., 18(2):, 2008.
[311]	Dieude, P., Guedj, M., Wipff, J., Avouac, J., Hachulla, E., Diot, E., Granel, B., Sibilia, J., Cabane, J., Meyer, O., Mouthon, L., Kahan, A., Boileau, C. & Allanore, Y. The PTPN22 620W allele confers susceptibility to Systemic Sclerosis: a large case control study among European Caucasians and meta-analysis. Rheumatism and Arthritis, 58():2183-2188, 2008.
[312]	Dieude, P., Guedj, M., Wipff, J., Avouac, J., Fajardy, I., Diot, E., Granel, B., Sibilia, J., Cabane, J., Mouthon, L., Cracowski, J., Carpentier, P., Hachulla, O., Kahan, A., Boileau, C. & Allanore, Y. Association between the IRF5 rs2004640 functional polymorphism and systemic sclerosis: A new perspective for pulmonary fibrosis. Arthritis Rheumatoid, 60():225-233, 2008.
[313]	Forner, K., Lamarine, M., Guedj, M., Dauvillier, J. & Wojcik, J. Universal false discovery rate estimation methodology for genome-wide association studies. Human Heredity, 65():183-194, 2008.
[314]	Guedj, M., Nuel, G. & Prum, B. A note on allelic tests in case-control association studies. Annals of Human Genetics, 72():407-409, 2008.
[315]	Guedj, M., Bourillon, A., Combadieres, C., Rodero, M., Dieude, P., Descamps, V., Dupin, N., Wolkenstein, P., Aegerter, P., Lebbe, C., Basset-Seguin, N., Prum, B., Saiag, P., Grandchamp, B. & Soufir, N. Variants of the MATP/SLC45A2 gene are protective for melanoma in the French population. Human Mutation, 29():1154-1160, 2008.
[316]	Zanghi, H., Ambroise, C. & Miele, V. Fast Online Graph Clustering via Erdös Renyi Mixture. Pattern Recognition, 41(12):3592-3599, 2008.
2007
[317]	Guedj, M. Méthodes statistiques pour l'analyse des données génétiques d'association à grande échelle. , 2007. (Theses)
[318]	Vescovo, L. Outils et méthodes pour la classification pyramidale des données biologiques. , 2007. (Theses)
[319]	Aschard, H., Guedj, M. & Demenais, F. A multiple-marker two-step approach for genome-wide association studies. BMC Proceedings, 1(S134):, 2007.
[320]	Avalos, M., Grandvalet, Y. & Ambroise, C. Parsimonious additive models. CSDA, 51(6):2851-2870, 2007.
[321]	Didier, G., Debomy, L., Pupin, M., Zhang, M., Grossmann, A., Devauchelle, C. & Laprevotte, I. Comparing sequences without alignments: application to HIV/SIV subtyping. BMC Bioinformatics, 8():1, 2007.
[322]	Garnier, S., Dieude, P., Michou, L., l, , Bardin, T., Prum, B. & Cornelis, F. IRF5 rs2004640-T allele, the new genetic factor for systemic lupus erythematosus, is not associated with rheumatoid arthritis. Ann. Rheum. Dis., 66():828-831, 2007.
[323]	Gaut, B., Wright, S., Rizzon, C., Dvorak, J. & Anderson, L. Recombination: an underappreciated factor in the evolution of plant genomes.. Nat Rev Genet., 8():77-84, 2007.
[324]	Guedj, M., Della-Chiesa, E., Picard, F. & Nuel, G. Computing power in case-control association studies through the use of quadratic approximations: application to meta-statistics. Annals of Human Genetics, 71():262-270, 2007.
[325]	Jacq, L., Garnier, S., Dieude, P., Michou, L., l, , Prum, B., Bardin, T. & Cornelis, F. The ITGAV rs3738919-C allele is associated with and linked to rheumatoid arthritis in the European Caucasian population: a family-based study. Arthritis Research \& Therapy, 9(R63):, 2007.
[326]	Michou, L., Lasbleiz, S., l, , Prum, B., Bardin, T., Dieude, P. & Cornelis, F. Linkage proof for PTPN22, the new rheumatoid arthritis susceptibility gene, a human autoimmunity gene. Proc. Natl. Acad. Sci. USA, 104():1649-1654, 2007.
[327]	Same, A., Ambroise, C. & Govaert, G. An online Classification EM algorithm based on the mixture model. Statistics and Computing, 17(3):209-218, 2007.
[328]	, (eds). Analyse Statistique des Séquences Biologiques. , (), Hermes Sciences, 2007.
[329]	Corel, E., El Feghali, R., Gerardin, F., Hoebeke, M., Nadal, M., Grossmann, A. & Devauchelle, C. Local Similarities and Clustering of Biological Sequences : New Insights from N-local Decoding. In The First International Symposium on Optimization and Systems Biology (OSB 2007), Lecture Notes in Operations Research(7):189-195, World Publishing, 2007.
[330]	Corel, E., El Feghali, R., Gerardin, F., Hoebeke, M., Nadal, M., Louis, A., Laprevotte, I., Grossmann, A. & Devauchelle, C. Local Similarities and Clustering of Biological Sequences. In Actes de JOBIM 2007, pages 69-71, 2007.
[331]	Guedj, M., Wojcik, J. & Nuel, G. Catching Local Replications: Use of the Local Score in Replicated Association Studies. In Proceedings of EMGM 2007 in Annals of Human Genetics, pages 550-559, 2007.
[332]	Lebarbier, E., Picard, F., Bundiska, E. & Robin, S. Joint segmentation of multivariate Gaussian processes using mixed linear models. In ASMDA 2007, 2007.
[333]	Picard, F., Daudin, J.J., Koskas, M., Schbath, S. & Robin, S. Assessing the exceptionality of network motifs. In Jobim 2007, 2007.
[334]	Aschard, H., Guedj, M. & Demenais, F. A multiple-marker two-step approach to genome-wide association studies. , 2007., (Oral presentation at the EMGM 2007, Heidelberg (Germany)).
[335]	Guedj, M. Local Replication: a local-score based approach to replicated association studies. , 2007., (Oral Presentation at the IGES 2007, York (UK)).
[336]	Guedj, M., Celisse, A., Robin, S. & Nuel, G. kerfdr: a semi-parametric kernel-based estimation of the local FDR. , 2007., (Poster ar IGES 2007, York (UK)).
[337]	Nuel, G. & Guedj, M. Statistical methods in genome-wide association studies. , 2007., (Colloque Génétique, Bioinformatique et Puces, Ermenonville (France)).
[338]	Wojcik, J., Guedj, M. & Forner, K. Exact p-value computation and exact False Discovery Rate estimation for genome-wide association studies. , 2007., (Poster at the HUGO HGM2007, Montreal (Canada)).
2006
[339]	Anderson, L., Lai, A., Stack, S., Rizzon, C. & Gaut, B. Uneven distribution of expressed sequence tag loci on maize pachytene chromosomes.. Genome Res., 16():115-22, 2006.
[340]	Didier, G., Laprevotte, I., Pupin, M. & Henaut, A. Local decoding of sequences and alignment-free comparison. Journal of Computational Biology, 13(8):1465-1476, 2006.
[341]	Drouaud, J., Camilleri, C., Bourguignon, P., l, , Prum, B., Quesneville, H. & Mezard, C. Free Full Text Variation in crossing-over rates across chromosome 4 of Arabidopsis thaliana reveals the presence of meiotic recombination “hot spots”.. Genome Research, 16():106-114, 2006.
[342]	Guedj, M., Wojcik, J., Della-Chiesa, E., Nuel, G. & Forner, K. A fast, unbiased and exact allelic test for case-control association studies. Human Heredity, 61():210-221, 2006.
[343]	Guedj, M., Robelin, D., Hoebeke, M., Lamarine, M., Wojcik, J. & Nuel, G. Detecting Local High-Scoring segments: a first-stage approach for genome-wide association studies. Stat App Genet Mol Bio, 5():1-16, 2006.
[344]	Matias, C., Schbath, S., Birmele, E., Daudin, J.J. & Robin, S. Networks motifs: mean and variance for the count. REVSTAT, 4(1):31-51, 2006.
[345]	Michou, L., Croiseau, P., l, , Prum, B., Clerget, F. & Cornelis, F. Confirmation of the Shared Epitope Allele Classification. Arthritis Research and Therapy, 28():79-, 2006.
[346]	Nicolas, P., Tocquet, A.S., Miele, V. & Muri, F. A Reversible Jump Markov Chain Monte-Carlo Algorithm for Bacterial Promoter Motifs Discovery. Journal of Computational Biology, 13(3):651-667, 2006.
[347]	Rizzon, C., Ponger, L. & Gaut, B. Striking similarities in the genomic distribution of tandemly arrayed genes in Arabidopsis and rice.. PLoS Comput Biol., 2(9):e115, 2006.
[348]	Zhu, X., Ambroise, C. & McLachlan, G. Selection bias in working with the top genes in supervised classification of tissue samples. Statistical Methodology, 3():29-41, 2006.
[349]	Prum, B. & Tocquet, A.S. The use of Markov Models and Hidden Markov Models in genomics. In Mathematical and computational methods in biology, (), Herman, 2006.
[350]	Daudin, J.J., Lacroix, V., Picard, F., Robin, S. & Sagot, M.F. Uncovering structure in biological networks. In JOBIM 2006, 2006.
[351]	Guedj, M., Della-Chiesa, E., Forner, K., Wojcik, J. & Nuel, G. Which alternatives to the biased allelic test in case-control association studies. In Proceedings of IGES 2006 in Genetic Epidemiology, 31:450-513, 2006.
[352]	Mariadassou, M., Daudin, J., Lacroix, V., Miele, V., Picard, F., Robin, S. & Sagot, M. Uncovering structure in biological networks. In Proceedings of RIAMS, 2006.
[353]	Miele, V., Vaillant, C., D'Aubenton, Y., Robelin, D., Prum, B. & Thermes, C. DNA sequence drives nucleosome occupancy of yeast promoters. In Proceeding of JOBIM, 2006.
[354]	Picard, F., Daudin, J.J., Schbath, S. & Robin, S. Assessing the exceptionality of network motifs. In Réseaux d'interactions : analyse, modélisation et simulation (RIAMS), 2006.
[355]	Prum, B. Búsqueda automática de los genes. , 2006.
[356]	Prum, B. Las matemáticas en la búsqueda de un gen responsable de cáncer. , 2006.
[357]	Prum, B. Statistique en Génomique. , 2006.
[358]	Aschard, H., Guedj, M. & Demenais, F. A three-step approach to genome-wide association studies.. , 2006., (Poster at GAW15, Tampa (USA)).
[359]	Della-Chiesa, E., Guedj, M. & Nuel, G. Should we combine association statistics in case-control association studies. , 2006., (Poster at EMGM 2006, Cardiff (UK)).
[360]	Guedj, M., Aschard, H. & Demenais, F. A three-step approach to genome-wide association studies.. , 2006., (GAW15 novel methods session, Tampa (USA)).
[361]	Guedj, M., Aschard, H., Wojcik, J., Nuel, G. & Demenais, F. Local Score statistic: picking-up candidates genomic regions in genome-wide association studies. , 2006., (Poster at the GAW15, Tampa (USA)).
[362]	Guedj, M., Della-Chiesa, E., Forner, K., Wojcik, J. & Nuel, G. Which alternatives to the biased allelic test in case-control association studies. , 2006., (Poster at EMGM 2006, Cardiff (UK)).
2005
[363]	Bekaert, M., Richard, H., Prum, B. & Rousset, J. Identification of programmed translational -1 frameshifting sites in the genome of Saccharomyces cerevisiae. Genome Research, 10():1411-1420, 2005.
[364]	Dieude, P., Garnier, S., Michou, L., l, , Prum, B. & Cornelis, F. Rheumatoid arthritis seropositive for the rheumatoid factor is linked to the protein tyrosine phosphatase nonreceptor 22-620W allele. Arthritis Research \& Therapy, 7():, 2005.
[365]	Picard, F., Robin, S., Lavielle, M., Vaisse, C. & Daudin, J.J. A statistical approach for array CGH data analysis. BMC Bioinformatics, 6(1):1-14, 2005.
[366]	Tezenas du Montcel, S., Michou, L., l, , Prum, B., Cornelis, F. & Clerget, F. New classification of HLA-DRB1 alleles support the shared epitope hypothesis of rheumatoid arthritis susceptibility. Arthritis Rheumatism, 52(1063-1068):, 2005.
[367]	Weyer-Menkoff, J., Devauchelle, C., Grossmann, A. & Grunewald, S. Integer linear programming as a tool for constructing trees from quartet data. Comput Biol Chem, 29(3):196-203, 2005.
[368]	Guedj, M. & Nuel, G. Local Score statistics: application to large scale association studies. In Proceedings of IGES 2005 in Genetic Epidemiology, 29:234-298, 2005.
[369]	Prum, B. Trouver les gènes coupables. , 2005.
[370]	Guedj, M. & Nuel, G. Local Score statistics: application to large scale association studies. , 2005., (Poster at IPG, Lyon (France)).
2004
[371]	Dieude, P., Osorio, J., l, , Prum, B. & Cornelis, F. A TNFR1 genotype with a protective role in familial rheumatoid arthritis.. Arthritis Rheumatism, 50():413-419, 2004.
[372]	Osorio , J., Bukulmez, H., Petit-Teixeira, E., Michou, L., l, , Prum, B., Olson, J. & Cornelis, F. Dense genome-wide linkage analysis of rheumatoid arthritis including covariates. Arthritis Rheumatism, 50():2557-2565, 2004.
[373]	, (eds). Analyzing microarray gene expression data. , (), Wiley, 2004.
[374]	Prum, B., Bourguignon, P., Guedj, M., Kepes, F., Matias, C., Nuel, G. & Omont, N. La recherche de gènes impliqués dans une maladie, collaboration avec Genset-Serono. , 2004., (Matapli 74 p23-41).
[375]	Guedj, M. & Nuel, G. Déséquilibre de liaison et association à la maladie dans les études de SNPs cas-témoins à échelle. , 2004., (Les industriels et les mathématiciens se parlent, IHP, Paris).
2003
[376]	Durot, C. & Tocquet, A.S. On the distance between the empirical process and its concave majorant in a monotone regression framework. Ann. I. H. Poincaré, Probabilités \\& Statistique, 39():217-240, 2003.
[377]	Goldstein, D., Fondrat, C., Muri, F., Nuel, G., Saragueta, P., Tocquet, A.S. & Prum, B. Short inverse complementary amino-acid sequences generate protein complexity. C. R. Acad. Sci. Biologie, 326():339-348, 2003.
[378]	Lerat, E., Rizzon, C. & Biemont, C. Sequence divergence within transposable element families in the Drosophila melanogaster genome.. Genome Res., 13():1889-96, 2003.
[379]	Leuteneger, A., Prum, B., Genin, E., Verny, C., Lemainque, A., Clerget, F. & Thompson, E. Estimation of the inbreeding coefficient through use of genomic data. Am. J. Hum. Genet., 73():516-523, 2003.
[380]	Rizzon, C., Martin, E., Marais, G., Duret, L., Segalat, L. & Biemont, C. Patterns of selection against transposons inferred from the distribution of Tc1, Tc3, and Tc5 insertions in the mut-7 line of the nematode Caenorhabditis elegans.. Genetics, 165():1127-1135, 2003.
[381]	Boyarinov, R.N., Chubarikov, V.N. & Ngongo, I.S. Asymptotic formulas for fractional moments of special sums. Chebyshevski\u\i\ Sb., 4(4(8)):173-183, 2003.
2002
[382]	Dieude, P., Petit, E., l, , Prum, B. & Cornelis, F. Association Between Tumor Necrosis Factor Receptor II and Familial, but Not Sporadic, Rheumatoid Arthritis. Arthritis Rheumatism, 46():2039-2044, 2002.
[383]	Nicolas, P., Bize, L., Muri, F., Hoebeke, M., Rodolphe, F., Ehrlich, S., Prum, B. & Bessieres, P. Mining Bacillus Subtilis chromosome heterogeneities using hidden Markov models. Nucleic Acids Research, 30():1418-1426, 2002.
[384]	Rizzon, C., Marais, G., Gouy, M. & Biemont, C. Recombination rate and the distribution of transposable elements in the Drosophila melanogaster genome.. Genome Res., 12():400-407, 2002.
[385]	Prum, B. Trouver un gène responsable du cancer. , 2002.
[386]	Miele, V., Nicolas, N., Muri, F. & Tocquet, A.S. Sélection de modèles de Markov cachés et hétérogénéité des séquences 3'UTR du génome humain. , 2002., (JOBIM poster).
2001
[387]	Durot, C. & Tocquet, A.S. Goodness of fit test for isotonic regression. ESAIM, Probabilités \\& Statistique, 5():119-140, 2001.
[388]	Muri, F. & Prum, B. Une approche statistique de l'analyse des génomes. La Gazette des Mathématiciens, 89():63-98, 2001.
[389]	Nuel, G., Robin, S. & Baril, C. Predicting distances using a linear model: the case of varietal distinctness. Journal of Applied Statistics, 28(5):607-627, 2001.
[390]	Tocquet, A.S. Likelihood based inference in nonlinear regression models using the p* and R* approach. Scandinavian Journal of Statistics, 28():429-443, 2001.
[391]	Sekowska, A., Robin, S., Daudin, J.J., Henaut, A. & Danchin, A. Extracting biological information from dna arrays : an unexpected link between arginine and methionine metabolism in bacillus subtilis. Genome Biology, 2(6):np, BioMed Central, 2001.
[392]	Nuel, G., Baril, C. & Robin, S. Varietal distinctness assisted by molecular markers: a methodological approach. In Acta Horiculturae, Proceedings of the MMH congresspages 65-71, 2001.
[393]	Prum, B. La recherche automatique de gènes. , 2001.
[394]	Prum, B. Mathématiques et biologie. , 2001.
2000
[395]	Goldstein, D., Muri, F., Saragueta, P. & Prum, B. Inverse Complementary homologues of cysteine signatures. CRAS Sciences de la Vie, 323(2):167-172, 2000.
[396]	Prum, B. Une approche statistique de l'analyse des séquences génétiques. , 2000.
[397]	Prum, B. Les chaînes de Markov dans l'analyse des génomes. , 2000.
[398]	Nuel, G., Robin, S. & Baril, C. Varietal distinctness assisted by molecular markers: a methodological approach. , 2000., (MMH (Molecular Markers in Horticulturae) Congress, Montpellier, France).
1999
[399]	Bize, L., Muri, F., Samson, F., Rodolphe, F., Ehrlich, S., Prum, B. & Bessieres, P. Searching gene transfers on Bacillus Subtilis using hidden Markov models. In Recomb'99 Proceedings of the Third Annual International Conference on Computational Molecular Biology, 1999.
[400]	Cot, C. Stochastic dichotomic models for digital video sequences. , 1999.
1998
[401]	Nuel, G., Robin, S. & Baril, C. Prédiction de distance phénotypiques à partir de données moléculaires pour la distinction variétale. , 1998., (Journées méthodologiques du GEVES (Groupe d'Etude et de Contrôle des Variétés et des Semences), Paris, France).