| Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies C Lazar, L Gatto, M Ferro, C Bruley, T Burger Journal of proteome research 15 (4), 1116-1125, 2016 | 469 | 2016 |
| DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics S Wieczorek, F Combes, C Lazar, QG Gianetto, L Gatto, A Dorffer, ... Bioinformatics 33 (1), btw580, 2017 | 280 | 2017 |
| Toward an axiomatic definition of conflict between belief functions S Destercke, T Burger IEEE transactions on cybernetics 43 (2), 585-596, 2013 | 157* | 2013 |
| Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata L Gatto, LM Breckels, S Wieczorek, T Burger, KS Lilley Bioinformatics 30 (9), 1322-1324, 2014 | 114 | 2014 |
| Deciphering thylakoid sub-compartments using a mass spectrometry-based approach M Tomizioli, C Lazar, S Brugière, T Burger, D Salvi, L Gatto, L Moyet, ... Molecular & Cellular Proteomics 13 (8), 2147-2167, 2014 | 109 | 2014 |
| Gentle Introduction to the Statistical Foundations of False Discovery Rate in Quantitative Proteomics T Burger Journal of proteome research 17 (1), 12-22, 2018 | 97 | 2018 |
| Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments Q Giai Gianetto, F Combes, C Ramus, C Bruley, Y Couté, T Burger PROTEOMICS 16 (1), 29-32, 2016 | 80 | 2016 |
| A belief-based sequential fusion approach for fusing manual signs and non-manual signals O Aran, T Burger, A Caplier, L Akarun Pattern Recognition 42 (5), 812-822, 2009 | 79 | 2009 |
| A foundation for reliable spatial proteomics data analysis L Gatto, LM Breckels, T Burger, DJH Nightingale, AJ Groen, C Campbell, ... Molecular & Cellular Proteomics 13 (8), 1937-1952, 2014 | 63 | 2014 |
| Beyond target-decoy competition: stable validation of peptide and protein identifications in mass spectrometry-based discovery proteomics Y Couté, C Bruley, T Burger Analytical Chemistry 92 (22), 14898–14906, 2020 | 56 | 2020 |
| A Dempster–Shafer Theory based combination of handwriting recognition systems with multiple rejection strategies Y Kessentini, T Burger, T Paquet Pattern Recognition 48 (2), 534-544, 2015 | 39 | 2015 |
| A consistency-specificity trade-off to select source behavior in information fusion F Pichon, S Destercke, T Burger IEEE transactions on cybernetics 45 (4), 598-609, 2014 | 39 | 2014 |
| Geometric views on conflicting mass functions: From distances to angles T Burger International Journal of Approximate Reasoning 70, 36-50, 2016 | 36* | 2016 |
| A peptide-level multiple imputation strategy accounting for the different natures of missing values in proteomics data QG Gianetto, S Wieczorek, Y Couté, T Burger bioRxiv, 2020.05. 29.122770, 2020 | 32 | 2020 |
| Cued speech hand gestures recognition tool T Burger, A Caplier, S Mancini 2005 13th European Signal Processing Conference, 1-4, 2005 | 31 | 2005 |
| How to randomly generate mass functions T Burger, S Destercke International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2013 | 28* | 2013 |
| Image and video for hearing impaired people A Caplier, S Stillittano, O Aran, L Akarun, G Bailly, D Beautemps, ... EURASIP Journal on Image and Video Processing 2007, 1-14, 2008 | 25 | 2008 |
| Modeling hesitation and conflict: a belief-based approach for multi-class problems T Burger, O Aran, A Caplier 2006 5th International Conference on Machine Learning and Applications …, 2006 | 24 | 2006 |
| Uses and misuses of the fudge factor in quantitative discovery proteomics Q Giai Gianetto, Y Couté, C Bruley, T Burger Proteomics 16 (14), 1955-1960, 2016 | 23 | 2016 |
| PerTurbo Manifold Learning Algorithm for Weakly Labeled Hyperspectral Image Classification L Chapel, T Burger, N Courty, S Lefèvre IEEE, 2014 | 23* | 2014 |
Yohann CoutéEDyP-lab, Research engineer Inserm, GrenobleEmail verificata su cea.fr