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Équipe statistique et génome

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Cette équipe du LaMME consituait le laboratoire Statistique et Génome avant le 1er janvier 2014.

Thématiques de recherche

Nos thèmes de recherche touchent aux statistiques et à la bio-informatique

  • tests multiples
  • apprentissage en grande dimension
  • sélection de modèles
  • statistique génétique
  • modèle d'évolution de séquences
  • gènes dupliqués

Voir les publications de l'équipe.

Missions

Notre but est de développer des méthodes originales pour l’analyse de données biologiques, issues majoritairement de la biologie moléculaire.

Plus précisément, les progrès immenses de la technologie inondent les banques de données d’une information très riche et très variée, que la communauté scientifique n’est même plus à même d’exploiter raisonnablement. Un exemple illustre les changements d’échelle dans l’obtention des données : le Projet Génome Humain – auquel participait la Génopole d’Évry –, lancé en 1990, a mis 14 ans pour obtenir une séquence “complète” de ce génome. Les techniques modernes permettent de séquencer le génome d’un individu en… deux jours !

Par ailleurs ces données se diversifient : à côté des séquences chromosomiques on dispose de séquences protéiques, d’informations de plus en plus précises sur leurs conformations dans l’espace ; mais aussi de “données d’expression” relatant quels gènes sont actifs dans les différentes conditions d’existence, donc quelles protéines sont présentes – voire en quelle quantité ; on a aussi compris que les gènes-à-protéines ne sont pas les seuls et que d’autres produisent toutes sortes d’ARN (les ARN messagers, ribosomiques, de transfert, les “small”-ARN, …), dont la présence ou la quantité sont mesurées ; on est capable de mesurer directement certaines interactions entre protéines (CHIP-chips, par exemple) ou entre diverses molécules ; ajoutons d’une part les données phénotypiques (comment réagit l’organisme) et les données bibliographiques qui rassemblent tout ce qui précède.

Il est clair que l’information pertinente ne peut être tirée de cet amas de données que si :

  1. on dispose d’ordinateurs puissants pour stocker et classer l’information ;
  2. on dispose de méthodes extrêmement performantes pour les traiter.

On a donc besoin de méthodes, fondant des algorithmes rapides et efficaces et de telles méthodes doivent bien sûr être conçues, améliorées et mises en œuvre sur la base d’analyses statistiques appropriées.

Notre équipe est par nature une équipe de Mathématiques. Elle est – jusqu’à présent – évaluée par la Commission 01 (Mathématiques) du CNRS et par le Conseil Scientifique du Département MIA (Mathématiques et Informatique Appliquées) de l’INRA. Son fonctionnement est donc celui d’une équipe de Mathématiques, dont le but premier est dans des revues, si possible de haut niveau, en Mathématiques, et d’intervenir dans des congrès et des colloques.

Mais c’est aussi une équipe que le CNRS a placée à l’interface avec la biologie. Comme toute équipe de Mathématiques Appliquées, elle a donc deux autres objectifs :

  1. rendre effectives et disponibles les méthodes élaborées théoriquement, c’est à dire implémenter des logiciels efficaces et conviviaux et promouvoir leur emploi par la communauté des biologistes ;
  2. interagir avec cette communauté, ce qui signifie accorder une importance aux résultats qui pourront être obtenus par les biologistes grâce à nos méthodes et nos logiciels ; en conséquence, il nous faut être à l’écoute de leurs problématiques et privilégier un développement ayant une pertinence pour le généticien à un développement menant à une belle théorie déconnectée de toute réalité.

Bien évidemment, le flux scientifique doit aller dans les deux sens, et cette démarche n’a de sens que si la résolution de problèmes de biologie induit en retour des développement internes à la discipline mathématique, à la démonstration de nouveaux résultats théoriques, à l’élaboration de nouvelles procédures validées par des démonstrations.

Membres actuels

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

NOM COMPLETStatutEquipePosteE-MailTutelle
ABRAICH YaoubDoctSG** **<@>UEVE
AMBROISE ChristophePRSG35 25<christophe.ambroise@genopole.cnrs.fr>UEVE
AMOUKOU SalimDoctSG** **<@>ENSIIE
BICHAT Antoine DoctSG** **<antoine.bichat@univ-evry.fr >UEVE
BRUNEL NicolasPRSG34 59<nicolas.brunel@ensiie.fr>ENSIIE
CHARANTONIS AnastaseMCFSG<aacharantonis@gmail.com>ENSIIE
CORREA MargotIESG34 51<margot.correa@math.cnrs.fr>CNRS
COT CécileMCFSG35 27<cecile. pantin@univ-evry.fr >UEVE
DALMASSO CyrilMCFSG35 30<cyril.dalmasso@univ-evry.fr>UEVE
DIAZ YolandeMCFSG35 39<ydiaz@genopole.cnrs.fr>UEVE
GUEDJ OdéliaDoctSG<@>UEVE
GUILLOUX AgathePRSG** **< agathe.guilloux@univ-evry.fr>UEVE
MURI-MAJOUBE FlorenceMCFSG35 51<florence.muri@genopole.cnrs.fr>IUT Paris 5
NEVORET CamilleDoctSG<c.nevoret@laposte.net>-
OBRY LudivineDoctSG** **<ludivine.obry@univ-evry.fr>UEVE
PARK JuhyunAISG<@>ENSIIE
PASCUCCI MarcoPostDocSG** **<marco.pascucci@univ-evry.fr>UEVE
RIGAILL GuillemCRSG<guillem.rigaill@inra.fr >INRA
RIZZON CarèneMCFSG35 40<carene.rizzon@genopole.cnrs.fr>UEVE
ROBIN GenevièveCRSG** **<genevieve.robin@inria.fr >CNRS
RUNGE VincentMCFSG** **<vincent.runge@univ-evry.fr >UEVE
SAMSON FranckIRSG35 37<franck.samson@inrae.fr>INRA
SANOU Do EdmondDoctSG** **<doedmond.sanou@univ-evry.fr >-
SAUTREUIL MathildeIESG** **<mathilde.sautreuil@ecp.fr>INRA
SZAFRANSKI MarieMCFSG35 52<marie.szafranski@math.cnrs.fr>ENSIIE
TAUPIN Marie-LucePRSG35 28<marie-luce.taupin@univ-evry.fr>UEVE
TOCQUET Anne-SophieMCFSG35 50<annesophie.tocquet@univ-evry.fr>UEVE

Invités 2016

(2 semaines en décembre).

(1 semaines en septembre).

Invités 2014

  • Ana Arribas-Gil, Universidad Carlos III, Madrid (2 semaines en janvier, 1 semaine en mai, 1 semaine en juin).

Invités 2013

Invités 2012

  • Bernd Klaus, Universität Leipzig (2 mois, Février à Mars).
  • Dave Campbell, Université Simon Fraser (1 mois en juin).
  • Oksana Chkrebtii, Université Simon Fraser (7 au 21 juin).
  • Kousuke Hanada, Riken Plant Science Center, Japan (1 mois en juin).

Invités 2011

  • Eric Kolaczyk, Boston University (3 mois à mi-temps avec l'ENSAE, du 15/09 au 15/12).

Invités 2010

Anciens membres

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

Nom nouvelle affectation
Devauchelle Claudine PR Univ. Angers
Lemler Sarah MCF Centrale-Supélec
Falconnet Mikael Professeur CPGE
Matias Catherine DR CNRS- UMR CNRS 7599
Whalley Justin
Kwemou Marius Post-doc
Le Floch Édith Centre National de Génotypage (CNG), CEA
Nguyen Van Hanh Université d'Agriculture de Hanoi
Birmelé Étienne PR, MAP5 Paris Descartes
Bouaziz Matthieu
Charbonnier Camille LITIS, Univ. Rouen
Jeanmougin Marine PostDoc Institut Curie
Latouche Pierre Maître de Conférences, Université Paris 1 Sorbonne
Grasseau Gilles Laboratoire Leprince-Ringuet
Zanghi Hugo ingénieur R&D, Exalead
Miele Vincent Laboratoire de Biométrie et Biologie Evolutive, UCB, Lyon
Picard Franck CR2 CNRS, Laboratoire de Biométrie et Biologie Evolutive, UCB, Lyon
Nuel Grégory CR1 CNRS, MAP5, Université Paris Descartes
Lèbre Sophie Maître de Conférences, LSIIT, Strasbourg
Vergne Nicolas Maître de Conférence, LMRS, Rouen
Hoebeke Mark INRA MaIAGE
Delorme Marie-Odile Atelier de BioInformatique - Université Paris VI
Guedj Mickaël Pharnext
Pasek Sophie Systématique adaptation et évolution, Université Paris 6
Richard Hugues Maître de Conférences, UPMC
Risler Jean-Loup retraite
Robelin David IR INRA, Toulouse
Slaoui Yousri Post-Doc, France Telecom

Publications

2020
[1]
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. implementation
[2]
2019
[3]
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):22, BioMed Central, 2019. implementation
[4]
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(1):1-16, BioMed Central, 2019. implementation
[5]
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. implementation
[6]
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. implementation
[7]
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. implementation
[8]
Fearnhead, P. & Rigaill, G. Changepoint detection in the presence of outliers. Journal of the American Statistical Association, 114(525):169-183, Taylor \& Francis, 2019. implementation
[9]
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. implementation
[10]
Blanchard, G., Neuvial, P. & Roquain, E. Post hoc confidence bounds on false positives using reference families. Annals of Statistics, Institute of Mathematical Statistics, 2019. implementation
[11]
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. implementation
[12]
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. implementation
[13]
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. implementation
[14]
Huet, S. & Taupin, M.L. Metamodel construction for sensitivity analysis. , 2019., (working paper or preprint). implementation
[15]
[16]
[17]
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). implementation
2018
[18]
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. implementation
[19]
Guinot, F., Szafranski, M., Ambroise, C. & Samson, F. Learning the optimal scale for GWAS through hierarchical SNP aggregation. BMC Bioinformatics, 19, BioMed Central, 2018. implementation
[20]
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). implementation
[21]
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. implementation
[22]
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. implementation
[23]
Robin, G., Ambroise, C. & Robin, S.S. Incomplete graphical model inference via latent tree aggregation. Statistical Modelling, 19(5):545-568, SAGE Publications, 2018. implementation
[24]
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. implementation
[25]
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). implementation
[26]
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. implementation
[27]
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. implementation
[28]
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. implementation
[29]
Hulot, A., Chiquet, J., Jaffrezic, F. & Rigaill, G. Fused-ANOVA : une méthode de clustering en grande dimension. In 50èmes Journées de Statistique, 2018. implementation
[30]
Laroche, B., Brunel, N., Goujot, D. & Labarthe, S. Parameter estimation for dynamical systems using an FDA approach. In International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018), pages np, 2018. implementation
[31]
Alaya, M.Z., Lemler, S., Guilloux, A. & Allart, T. High-dimensional time-varying Aalen and Cox models. , 2018., (working paper or preprint). implementation
[32]
Virouleau, A., Guilloux, A., Gaiffas, S. & Bogdan, M. HIGH-DIMENSIONAL ROBUST REGRESSION AND OUTLIERS DETECTION WITH SLOPE. , 2018., (working paper or preprint). implementation
2017
[33] (Theses)
[34]
Stanislas, V., Dalmasso, C. & Ambroise, C. Eigen-Epistasis for detecting Gene-Gene interactions. BMC Bioinformatics, 18:54, BioMed Central, 2017. implementation
[35]
Stanislas, V., Dalmasso, C. & Ambroise, C. Eigen-Epistasis for detecting gene-gene interactions. BMC Bioinformatics, 18:np, BioMed Central, 2017. implementation
[36]
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). implementation
[37]
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. implementation
[38]
Huet, S. & Taupin, M.L. Metamodel construction for sensitivity analysis. ESAIM: Proceedings and Surveys, 60-2017:27-69, EDP Sciences, 2017. implementation
[39]
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. implementation
[40]
Allart, T., Levieux, G., Pierfitte, M., Guilloux, A. & Natkin, S. Difficulty influence on motivation over time in video games using survival analysis. In FDG - Foundation of Digital Games, 6, ACM Press, 2017. implementation
[41]
Tabouy, T., BARBILLON, P. & Chiquet, J. Variational Inference for Stochastic Block Models from Sampled Data. , 2017., (working paper or preprint). implementation
[42]
Alaya, M.Z., Bussy, S., Gaiffas, S. & Guilloux, A. Binarsity: a penalization for one-hot encoded features. , 2017., (working paper or preprint). implementation
[43]
Bussy, S., Guilloux, A., Gaiffas, S. & Jannot, A.S. C-mix: a high dimensional mixture model for censored durations, with applications to genetic data. , 2017., (working paper or preprint). implementation
2016
[44] (Theses)
[45] (Theses)
[46]
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. implementation
[47]
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, 2016. implementation
[48]
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. implementation
[49]
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. implementation
[50]
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. implementation
[51]
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. implementation
[52]
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.A. Learning ecological networks from next-generation sequencing data. In Ecosystem Services: From Biodiversity to Society, Part 2, 54:np, 2016. implementation
[53]
[54]
[55]
Celisse, A., Marot, G., Pierre-Jean, M. & Rigaill, G. New efficient algorithms for multiple change-point detection with kernels. , 2016., (working paper or preprint). implementation
2015
[56]
[57]
[58] (Theses)
[59]
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.
[60]
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. implementation
[61]
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. implementation
[62]
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. implementation
[63]
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. implementation
[64]
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. implementation
[65]
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. implementation
[66]
Latouche, P., Birmele, E. & Ambroise, C. Handbook of Mixed Membership Models and Their Applications. , pages 547-568Chapman and Hall, 2015.
[67]
[68]
Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Significance testing for variable selection in high-dimension. In Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pages 1-8, IEEE, 2015. implementation
[69]
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). implementation
[70]
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. implementation
[71]
[72]
[73]
Becu, J.M., Grandvalet, Y., Ambroise, C. & Dalmasso, C. Beyond Support in Two-Stage Variable Selection. , 2015., (working paper or preprint). implementation
[74]
Kwemou, M., Taupin, M.L. & Tocquet, A.S. MODEL SELECTION IN LOGISTIC REGRESSION. , 2015., (working paper or preprint). implementation
[75]
Brouard, C., d'Alche-Buc, F. & Szafranski, M. Input Output Kernel Regression. , 2015., (working paper or preprint). implementation
[76]
[77]
2014
[78] (Theses)
[79] (Theses)
[80]
Latouche, P., Birmele, E. & Ambroise, C. Model Selection in Overlapping Stochastic Block Models. Electronic Journal of Statistics, 8:762-794, 2014.
[81]
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). implementation
[82]
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. implementation
[83]
Pierre-Jean, M., Rigaill, G. & Neuvial, P. Performance evaluation of DNA copy number segmentation methods. Briefings in Bioinformatics, 2014. implementation
[84]
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. implementation
[85]
Matias, C. & Robin, S. Modeling heterogeneity in random graphs through latent space models: a selective review. ESAIM: Proceedings, 47:55-74, EDP Sciences, 2014. implementation
[86]
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, G., Renault, V., Bayer, P.E., Golicz, A.A., Manoli, S., Lee, T.H., Dinh Thi, V.H., chalabi, s., Hu, Q., Fan, C., 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., Batley, J., Snowdon, R.J., Tost, J., Edwards, D., Zhou, Y., Hua, W., Sharpe, A.G., Paterson, A.H., Guan, C. & Wincker, P. Early allopolyploid evolution in the post-Neolithic[i] Brassica napus[/i] oilseed genome. Science, 345(6199):950-953, American Association for the Advancement of Science, 2014. implementation
[87]
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, Public Library of Science, 2014. implementation
[88]
Dedecker, J., Samson, A. & Taupin, M.L. Estimation in autoregressive model with measurement error. ESAIM: Probability and Statistics, 18:277-307, EDP Sciences, 2014. implementation
[89]
Jeanmougin, M., Charbonnier, C., Guedj, M. & Chiquet, J. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. , ():xxx, Oxford University Press, 2014.
[90]
Chazottes, J., Cuny, C., Dedecker, J., Fan, X. & Lemler, S. Limit theorems and inequalities via martingale methods. In ESAIM: Proceedings, 44():177-196, , 2014.
[91]
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.
[92]
[93]
[94]
Brunel, N.J.B. & Clairon, Q. A Tracking Approach to Parameter Estimation in Linear Ordinary Differential Equations. , 2014., (working paper or preprint). implementation
[95]
2013
[96]
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. implementation
[97]
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.
[98]
Neuvial, P. Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators. Journal of Machine Learning Research, 14():1423\−1459, 2013. implementation
[99]
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. implementation
[100]
Chiquet, J. & Limnios, N. Stochastic Reliability and Maintenance Modeling. , 9():, Springer, 2013.
[101]
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.
[102]
[103]
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.
[104]
[105]
[106]
[107]
Pierre-Jean, M. Change-point detection with kernel methods : application to DNA copy number signals. , 2013., (45e Journées de Statistiques de la SFDS , Toulouse).
[108]
2012
[109] (Theses)
[110]
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.
[111]
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. implementation
[112]
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.
[113]
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. implementation
[114]
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. implementation
[115]
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. implementation
[116]
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. implementation
[117]
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.
[118]
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. implementation
[119]
Latouche, P., Birmele, E. & Ambroise, C. Variational Bayesian Inference and Complexity Control for Stochastic Block Models. Statistical Modelling, 12(1):93-115, 2012. implementation
[120]
Neuvial, P. & Roquain, E. On False Discovery Rate thresholding for classification under sparsity. Annals of Statistics, 40(5):2572-2600, 2012. implementation
[121]
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. implementation
[122]
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.
[123]
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.
[124]
[125]
[126]
[127]
[128]
Lim, T., y, , Sahut, J.M. & Scotti, S. Bid-ask spread modelling, a perturbation approach. , 2012.
[129]
Pierre-Jean, M. Segmentation de données génomiques en cancérologie. , 2012., (Journée annuelle du groupe Biopharmacie et Santé de la SFDS).
2011
[130]
[131]
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. implementation
[132]
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.
[133]
Chiquet, J., Grandvalet, Y. & Ambroise, C. Inferring Multiple Graphical Structures. Statistics and Computing, 21(4):537-553, 2011. implementation
[134]
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. implementation
[135]
Jeanmougin, M., Guedj, M. & Ambroise, C. Defining a robust biological prior from Pathway Analysis to drive Network Inference.. J-SFdS, 152(2):, 2011. implementation
[136]
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.
[137]
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. implementation
[138]
Neuvial, P., Bengtsson, H. & Speed, T. Handbook of Statistical Bioinformatics. , ():225-255, Springer, 2011. implementation
[139]
Chiquet, J., Grandvalet, Y. & Charbonnier, C. Sparsity with sign-coherent groups of variables via the cooperative-Lasso. In Proceedings of SPARS'11, Edimburgh, ():, , 2011.
[140]
Chiquet, J. Réseaux biologiques. , 2011., (La gazette des mathématiciens No. 130 pp. 76--82). implementation
[141]
Neuvial, P. Tests multiples en génomique. , 2011., (La gazette des mathématiciens No. 130 pp. 71-76). implementation
[142]
2010
[143]
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. implementation
[144]
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). implementation
[145]
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. implementation
[146]
Pitschi, F., Devauchelle, C. & Corel, E. Automatic detection of anchor points for multiple sequence alignment. BMC Bioinformatics, 11(445):, 2010. implementation
[147]
Zanghi, H., Picard, F., Miele, V. & Ambroise, C. Strategies for Online Inference of Network Mixture. Annals of Applied Statistics, 4(2):687-714, 2010. implementation
[148]
Zanghi, H., Volant, S. & Ambroise, C. Clustering based on random graph model embedding vertex features. Pattern Recognition Letters, 31(9):830-836, 2010.
[149]
, (eds). La démarche statistique. , (), Cépaduès, 2010.
[150]
Charbonnier, C., Chiquet, J. & Ambroise, C. Weighted-Lasso for Structured Network Inference for Time-Course data. In JOBIM'10, Montpellier, 2010.
[151]
Chiquet, J., Grandvalet, Y. & Ambroise, C. Inferring Multiple Graphical Structures. In Workshop MODGRAPHII, JOBIM'10, Montpellier, 2010.
[152]
Grandvalet, Y., Chiquet, J. & Ambroise, C. Inferring Multiple Regulation Networks. In Proceedings of the MLCB NIPS'10 Workshop, Vancouver, 2010.
[153]
Grandvalet, Y., Chiquet, J. & Ambroise, C. Inférence jointe de la structure de modèles graphiques gaussiens. In actes de CAp'10, Clermont-Ferrand, 2010.
2009
[154]
Ambroise, C., Chiquet, J. & Matias, C. Inferring sparse Gaussian graphical models with latent structure. Electronic Journal of Statistics, 3():205-238, 2009. implementation
[155]
Chiquet, J., Smith, A., Grasseau, G., Matias, C. & Ambroise, C. SIMoNe: Statistical Inference for MOdular NEtworks. Bioinformatics, 25(3):417-418, 2009. implementation
[156]
Durot, C., Lebarbier, E. & Tocquet, A.S. Estimating the joint distribution of independent categorical variables via model selection. Bernoulli, 15(2):475-507, 2009.
[157]
Guedj, M., Celisse, A., Robin, S. & Nuel, G. kerfdr: A semi-parametric kernel-based approach to local FDR estimations.. BMC Bioinformatics, 10():84+, 2009. implementation
[158]
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.
[159]
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.
[160]
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. implementation
[161]
Ambroise, C. & Dang, M. Data Analysis. , ():289-318, Wiley, 2009.
[162]
Latouche, P., Birmele, E. & Ambroise, C. Advances in Data Analysis, Data Handling and Business Intelligence. , ():229-239, springer, 2009. implementation
[163]
Chiquet, J., Charbonnier, C. & Ambroise, C. SIMoNe : Statistical Inference of Modular Network. In Workshop MODGRAPH, JOBIM'09, Nantes, 2009.
[164]
Latouche, P., Birmele, E. & Ambroise, C. Uncovering overlapping clusters in biological networks. In Journées ouvertes en biologie, informatique et mathématiques (Jobim). Nantes, 2009.
2008
[165]
Birmele, E., Elati, M., Rouveirol, C. & Ambroise, C. Identification of functional modules based on transcriptional regulation structure. BMC Proceedings, 2((Suppl 4):S4):, 2008.
[166]
Daudin, J.J., Picard, F. & Robin, S. A mixture model for random graphs. Statist. Comput., 18(2):, 2008.
[167]
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.
[168]
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. implementation
[169]
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. implementation
[170]
Guedj, M., Nuel, G. & Prum, B. A note on allelic tests in case-control association studies. Annals of Human Genetics, 72():407-409, 2008. implementation
[171]
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. implementation
[172]
Zanghi, H., Ambroise, C. & Miele, V. Fast Online Graph Clustering via Erdös Renyi Mixture. Pattern Recognition, 41(12):3592-3599, 2008.
2007
[173] (Theses)
[174]
Aschard, H., Guedj, M. & Demenais, F. A multiple-marker two-step approach for genome-wide association studies. BMC Proceedings, 1(S134):, 2007. implementation
[175]
Avalos, M., Grandvalet, Y. & Ambroise, C. Parsimonious additive models. CSDA, 51(6):2851-2870, 2007.
[176]
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.
[177]
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.
[178]
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.
[179]
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. implementation
[180]
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.
[181]
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.
[182]
Same, A., Ambroise, C. & Govaert, G. An online Classification EM algorithm based on the mixture model. Statistics and Computing, 17(3):209-218, 2007.
[183]
, (eds). Analyse Statistique des Séquences Biologiques. , (), Hermes Sciences, 2007.
[184]
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. implementation
[185]
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.
[186]
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. implementation
[187]
Lebarbier, E., Picard, F., Bundiska, E. & Robin, S. Joint segmentation of multivariate Gaussian processes using mixed linear models. In ASMDA 2007, 2007.
[188]
Picard, F., Daudin, J.J., Koskas, M., Schbath, S. & Robin, S. Assessing the exceptionality of network motifs. In Jobim 2007, 2007.
[189]
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)).
[190]
Guedj, M. Local Replication: a local-score based approach to replicated association studies. , 2007., (Oral Presentation at the IGES 2007, York (UK)).
[191]
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)).
[192]
Nuel, G. & Guedj, M. Statistical methods in genome-wide association studies. , 2007., (Colloque Génétique, Bioinformatique et Puces, Ermenonville (France)).
[193]
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)). implementation
2006
[194]
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.
[195]
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.
[196]
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. implementation
[197]
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. implementation
[198]
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. implementation
[199]
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.
[200]
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.
[201]
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.
[202]
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.
[203]
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.
[204]
Daudin, J.J., Lacroix, V., Picard, F., Robin, S. & Sagot, M.F. Uncovering structure in biological networks. In JOBIM 2006, 2006.
[205]
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. implementation
[206]
Mariadassou, M., Daudin, J., Lacroix, V., Miele, V., Picard, F., Robin, S. & Sagot, M. Uncovering structure in biological networks. In Proceedings of RIAMS, 2006.
[207]
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.
[208]
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.
[209]
[210]
[211]
[212]
Aschard, H., Guedj, M. & Demenais, F. A three-step approach to genome-wide association studies.. , 2006., (Poster at GAW15, Tampa (USA)).
[213]
Della-Chiesa, E., Guedj, M. & Nuel, G. Should we combine association statistics in case-control association studies. , 2006., (Poster at EMGM 2006, Cardiff (UK)).
[214]
Guedj, M., Aschard, H. & Demenais, F. A three-step approach to genome-wide association studies.. , 2006., (GAW15 novel methods session, Tampa (USA)).
[215]
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)).
[216]
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
[217]
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.
[218]
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.
[219]
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.
[220]
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.
[221]
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.
[222]
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. implementation
[223]
[224]
Guedj, M. & Nuel, G. Local Score statistics: application to large scale association studies. , 2005., (Poster at IPG, Lyon (France)).
2004
[225]
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.
[226]
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.
[227]
, (eds). Analyzing microarray gene expression data. , (), Wiley, 2004. implementation
[228]
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).
[229]
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
[230]
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.
[231]
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.
[232]
Lerat, E., Rizzon, C. & Biemont, C. Sequence divergence within transposable element families in the Drosophila melanogaster genome.. Genome Res., 13():1889-96, 2003.
[233]
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.
[234]
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.
2002
[235]
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.
[236]
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.
[237]
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.
[238]
[239]
2001
[240]
Durot, C. & Tocquet, A.S. Goodness of fit test for isotonic regression. ESAIM, Probabilités \\& Statistique, 5():119-140, 2001.
[241]
Muri, F. & Prum, B. Une approche statistique de l'analyse des génomes. La Gazette des Mathématiciens, 89():63-98, 2001.
[242]
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.
[243]
Tocquet, A.S. Likelihood based inference in nonlinear regression models using the p* and R* approach. Scandinavian Journal of Statistics, 28():429-443, 2001.
[244]
durot, C. & Tocquet, A.S. Goodness of fit test for isotonic regression. ESAIM: Probability and Statistics, 5:119-140, EDP Sciences, 2001. implementation
[245]
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.
[246]
[247]
2000
[248]
Goldstein, D., Muri, F., Saragueta, P. & Prum, B. Inverse Complementary homologues of cysteine signatures. CRAS Sciences de la Vie, 323(2):167-172, 2000.
[249]
[250]
[251]
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
[252]
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.
[253]
1998
[254]
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).
sg/welcome.txt · Last modified: 2017/11/21 15:35 by Christophe Ambroise

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