| 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), 22, 2017 | 225 | 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 | 130 | 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 | 102 | 2011 |
| 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 | 86 | 2016 |
| Liquid-chromatography retention order prediction for metabolite identification E Bach, S Szedmak, C Brouard, S Böcker, J Rousu Bioinformatics 34 (17), i875-i883, 2018 | 83 | 2018 |
| 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 | 43 | 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 |
| 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 | 21 | 2019 |
| 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), 273, 2013 | 21 | 2013 |
| Pushing data into CP models using graphical model learning and solving C Brouard, S de Givry, T Schiex International Conference on Principles and Practice of Constraint …, 2020 | 18 | 2020 |
| 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 | 14 | 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 | 12 | 2022 |
| 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 |
| Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique C Brouard Université d'Evry-Val d'Essonne, 2013 | 12 | 2013 |
| Should we really use graph neural networks for transcriptomic prediction? C Brouard, R Mourad, N Vialaneix Briefings in bioinformatics 25 (2), 2024 | 9 | 2024 |
| Soft kernel target alignment for two-stage multiple kernel learning H Shen, S Szedmak, C Brouard, J Rousu International Conference on Discovery Science, 427-441, 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 | |
| NMFProfiler: a multi-omics integration method for samples stratified in groups A Mercadié, É Gravier, G Josse, I Fournier, C Viodé, N Vialaneix, ... Bioinformatics 41 (2), btaf066, 2025 | 3 | 2025 |
| 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 |
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisVerified email at telecom-paris.fr
