| Critical assessment of small molecule identification 2016: automated methods EL Schymanski, C Ruttkies, M Krauss, C Brouard, T Kind, K Dührkop, ... Journal of cheminformatics 9, 1-21, 2017 | 200 | 2017 |
| Fast metabolite identification with input output kernel regression C Brouard, H Shen, K Dührkop, F d'Alché-Buc, S Böcker, J Rousu Bioinformatics 32 (12), i28-i36, 2016 | 109 | 2016 |
| Semi-supervised penalized output kernel regression for link prediction C Brouard, F d'Alché-Buc, M Szafranski Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 98 | 2011 |
| Liquid-chromatography retention order prediction for metabolite identification E Bach, S Szedmak, C Brouard, S Böcker, J Rousu Bioinformatics 34 (17), i875-i883, 2018 | 73 | 2018 |
| Input output kernel regression: Supervised and semi-supervised structured output prediction with operator-valued kernels C Brouard, M Szafranski, F d'Alché-Buc Journal of Machine Learning Research 17 (176), 1-48, 2016 | 73 | 2016 |
| Learning to predict graphs with fused gromov-wasserstein barycenters L Brogat-Motte, R Flamary, C Brouard, J Rousu, F d’Alché-Buc International Conference on Machine Learning, 2321-2335, 2022 | 31 | 2022 |
| Magnitude-preserving ranking for structured outputs C Brouard, E Bach, S Böcker, J Rousu Asian Conference on Machine Learning, 407-422, 2017 | 23 | 2017 |
| Learning a Markov Logic network for supervised gene regulatory network inference C Brouard, C Vrain, J Dubois, D Castel, MA Debily, F d’Alché-Buc BMC bioinformatics 14, 1-14, 2013 | 20 | 2013 |
| Improved small molecule identification through learning combinations of kernel regression models C Brouard, A Bassé, F d’Alché-Buc, J Rousu Metabolites 9 (8), 160, 2019 | 19 | 2019 |
| Pushing data into CP models using graphical model learning and solving C Brouard, S de Givry, T Schiex Principles and Practice of Constraint Programming: 26th International …, 2020 | 14 | 2020 |
| Machine learning of protein interactions in fungal secretory pathways J Kludas, M Arvas, S Castillo, T Pakula, M Oja, C Brouard, J Jäntti, ... PloS one 11 (7), e0159302, 2016 | 12 | 2016 |
| Vector-valued least-squares regression under output regularity assumptions L Brogat-Motte, A Rudi, C Brouard, J Rousu, F d'Alché-Buc Journal of Machine Learning Research 23 (344), 1-50, 2022 | 10 | 2022 |
| Feature selection for kernel methods in systems biology C Brouard, J Mariette, R Flamary, N Vialaneix NAR genomics and bioinformatics 4 (1), lqac014, 2022 | 9 | 2022 |
| Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique C Brouard Université d'Evry-Val d'Essonne, 2013 | 9 | 2013 |
| Should we really use graph neural networks for transcriptomic prediction? C Brouard, R Mourad, N Vialaneix Briefings in bioinformatics 25 (2), bbae027, 2024 | 4 | 2024 |
| Soft kernel target alignment for two-stage multiple kernel learning H Shen, S Szedmak, C Brouard, J Rousu Discovery Science: 19th International Conference, DS 2016, Bari, Italy …, 2016 | 4 | 2016 |
| Critical assessment of small molecule identification 2016: automated methods. J Cheminform. 2017; 9 (1): 22 EL Schymanski, C Ruttkies, M Krauss, C Brouard, T Kind, K Dührkop | 4 | |
| Learning output embeddings in structured prediction L Brogat-Motte, A Rudi, C Brouard, J Rousu, F d'Alché-Buc arXiv preprint arXiv:2007.14703, 2020 | 3 | 2020 |
| RNA expression dataset of 384 sunflower hybrids in field condition C Penouilh-Suzette, L Pomiès, H Duruflé, N Blanchet, F Bonnafous, ... OCL 27, 36, 2020 | 3 | 2020 |
| Regularized output kernel regression applied to protein-protein interaction network inference C Brouard, M Szafranski, F d’Alché-Buc NIPS MLCB Workshop, 2010 | 3 | 2010 |
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisVerified email at telecom-paris.fr
