| Group lasso with overlap and graph lasso L Jacob, G Obozinski, JP Vert Proceedings of the 26th annual international conference on machine learning …, 2009 | 1172 | 2009 |
| Clustered multi-task learning: A convex formulation L Jacob, J Vert, F Bach Advances in neural information processing systems 21, 2008 | 584 | 2008 |
| Protein-ligand interaction prediction: an improved chemogenomics approach L Jacob, JP Vert bioinformatics 24 (19), 2149-2156, 2008 | 429 | 2008 |
| Group lasso with overlaps: the latent group lasso approach G Obozinski, L Jacob, JP Vert arXiv preprint arXiv:1110.0413, 2011 | 228 | 2011 |
| A fast and agnostic method for bacterial genome-wide association studies: bridging the gap between kmers and genetic events M Jaillard, L Lima, M Tournoud, P Mahé, A van Belkum, V Lacroix, ... PLOS Genetics 14 (11), 1-28, 2018 | 204 | 2018 |
| Statistical methods for handling unwanted variation in metabolomics data AMD Livera, M Sysi-Aho, L Jacob, JA Gagnon-Bartsch, S Castillo, ... Analytical chemistry 87 (7), 3606-3615, 2015 | 198 | 2015 |
| A more powerful two-sample test in high dimensions using random projection M Lopes, L Jacob, MJ Wainwright Advances in Neural Information Processing Systems 24, 2011 | 171 | 2011 |
| Virtual screening of GPCRs: An in silico chemogenomics approach L Jacob, B Hoffmann, V Stoven, JP Vert BMC bioinformatics 9, 1-16, 2008 | 140 | 2008 |
| Efficient peptide–MHC-I binding prediction for alleles with few known binders L Jacob, JP Vert Bioinformatics 24 (3), 358-366, 2008 | 129 | 2008 |
| More power via graph-structured tests for differential expression of gene networks L Jacob, P Neuvial, S Dudoit | 128* | 2012 |
| Removing unwanted variation from high dimensional data with negative controls JA Gagnon-Bartsch, L Jacob, TP Speed Berkeley: Tech Reports from Dep Stat Univ California, 1-112, 2013 | 123 | 2013 |
| Efficient RNA isoform identification and quantification from RNA-Seq data with network flows E Bernard, L Jacob, J Mairal, JP Vert Bioinformatics 30 (17), 2447-2455, 2014 | 113 | 2014 |
| Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed L Jacob, JA Gagnon-Bartsch, TP Speed Biostatistics 17 (1), 16-28, 2016 | 110 | 2016 |
| Search for a gene expression signature of breast cancer local recurrence in young women N Servant, MA Bollet, H Halfwerk, K Bleakley, B Kreike, L Jacob, D Sie, ... Clinical Cancer Research 18 (6), 1704-1715, 2012 | 87 | 2012 |
| Immunosenescence patterns differ between populations but not between sexes in a long-lived mammal L Cheynel, JF Lemaître, JM Gaillard, B Rey, G Bourgoin, H Ferté, M Jégo, ... Scientific reports 7 (1), 1-11, 2017 | 68 | 2017 |
| Convolutional kernel networks for graph-structured data D Chen, L Jacob, J Mairal International Conference on Machine Learning, 1576-1586, 2020 | 66 | 2020 |
| Machine learning for in silico virtual screening and chemical genomics: new strategies JP Vert, L Jacob Combinatorial chemistry & high throughput screening 11 (8), 677-685, 2008 | 65 | 2008 |
| The cost of growing large: Costs of post‐weaning growth on body mass senescence in a wild mammal F Douhard, JM Gaillard, M Pellerin, L Jacob, JF Lemaître Oikos 126 (9), 1329-1338, 2017 | 50 | 2017 |
| Niche specialization and spread of Staphylococcus capitis involved in neonatal sepsis T Wirth, M Bergot, JP Rasigade, B Pichon, M Barbier, P Martins-Simoes, ... Nature Microbiology 5 (5), 735-745, 2020 | 47 | 2020 |
| Combining calls from multiple somatic mutation-callers SY Kim, L Jacob, TP Speed BMC bioinformatics 15, 1-8, 2014 | 39 | 2014 |
Jean-Philippe VertOwkin / PSL UniversityDirección de correo verificada de owkin.com
