| Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models A Rau, C Maugis-Rabusseau, ML Martin-Magniette, G Celeux Bioinformatics 31 (9), 1420-1427, 2015 | 88 | 2015 |
| Transformation and model choice for RNA-seq co-expression analysis A Rau, C Maugis-Rabusseau Briefings in bioinformatics 19 (3), 425-436, 2018 | 87 | 2018 |
| Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis G Rigaill, S Balzergue, V Brunaud, E Blondet, A Rau, O Rogier, J Caius, ... Briefings in bioinformatics 19 (1), 65-76, 2018 | 62 | 2018 |
| Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data A Godichon-Baggioni, C Maugis-Rabusseau, A Rau Journal of Applied Statistics 46 (1), 47-65, 2019 | 58 | 2019 |
| Comparing model selection and regularization approaches to variable selection in model-based clustering G Celeux, ML Martin-Magniette, C Maugis-Rabusseau, AE Raftery Journal de la Societe francaise de statistique 155 (2), 57-71, 2014 | 42 | 2014 |
| Variable selection in model-based clustering and discriminant analysis with a regularization approach G Celeux, C Maugis-Rabusseau, M Sedki Advances in Data Analysis and Classification 13, 259-278, 2019 | 39 | 2019 |
| Adaptive density estimation for clustering with Gaussian mixtures C Maugis-Rabusseau, B Michel ESAIM: Probability and Statistics 17, 698-724, 2013 | 38 | 2013 |
| On the estimation of mixtures of Poisson regression models with large number of components P Papastamoulis, ML Martin-Magniette, C Maugis-Rabusseau Computational Statistics & Data Analysis 93, 97-106, 2016 | 32 | 2016 |
| Clustering high-throughput sequencing data with Poisson mixture models A Rau, G Celeux, ML Martin-Magniette, C Maugis-Rabusseau Inria, 2011 | 27 | 2011 |
| A sparse variable selection procedure in model-based clustering C Meynet, C Maugis-Rabusseau | 21 | 2012 |
| Parameter recovery in two-component contamination mixtures: The strategy S Gadat, J Kahn, C Marteau, C Maugis-Rabusseau | 14 | 2020 |
| SuperMix: sparse regularization for mixtures Y De Castro, S Gadat, C Marteau, C Maugis-Rabusseau The Annals of Statistics 49 (3), 1779-1809, 2021 | 11 | 2021 |
| Non-asymptotic detection of two-component mixtures with unknown means B Laurent, C Marteau, C Maugis-Rabusseau | 11 | 2016 |
| Multidimensional two-component Gaussian mixtures detection B Laurent, C Marteau, C Maugis-Rabusseau | 8 | 2018 |
| SelvarClustMV: Variable selection approach in model-based clustering allowing for missing values C Maugis-Rabusseau, ML Martin-Magniette, S Pelletier Journal de la Société Française de Statistique 153 (2), 21-36, 2012 | 7 | 2012 |
| SelvarMix: AR package for variable selection in model-based clustering and discriminant analysis with a regularization approach M Sedki, G Celeux, C Maugis-Rabusseau INRIA Techical report, 2014 | 6 | 2014 |
| SelvarMix: Regularization for variable selection in model-based clustering and discriminant analysis M Sedki, G Celeux, C Maugis-Rabusseau R package version 1 (1), 2017 | 5 | 2017 |
| The DendrisCHIP® Technology as a New, Rapid and Reliable Molecular Method for the Diagnosis of Osteoarticular Infections E Bernard, T Peyret, M Plinet, Y Contie, T Cazaudarré, Y Rouquet, ... Diagnostics 12 (6), 1353, 2022 | 4 | 2022 |
| Insights on the control of yeast single-cell growth variability by members of the trehalose phosphate synthase (TPS) complex S Arabaciyan, M Saint-Antoine, C Maugis-Rabusseau, JM François, ... Frontiers in Cell and Developmental Biology 9, 607628, 2021 | 4 | 2021 |
| IGLOO: An Iterative Global Exploration and Local Optimization Algorithm to Find Diverse Low-Energy Conformations of Flexible Molecules W Margerit, A Charpentier, C Maugis-Rabusseau, JC Schön, N Tarrat, ... Algorithms 16 (10), 476, 2023 | 3 | 2023 |
