| 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), 1-21, 2017 | 100 | 2017 |
| 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 | 78 | 2011 |
| 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 | 53 | 2016 |
| 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, np, 2016 | 41 | 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 | 22 | 2018 |
| 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), 1-14, 2013 | 16 | 2013 |
| Magnitude-preserving ranking for structured outputs C Brouard, E Bach, S Böcker, J Rousu Asian Conference on Machine Learning, 407-422, 2017 | 10 | 2017 |
| 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 | 7 | 2016 |
| Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique C Brouard Université d'Évry-Val-d'Essonne, 2013 | 6 | 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 | 3 | 2019 |
| Critical assessment of small molecule identification 2016: automated methods. J Cheminform 9 (1): 22 EL Schymanski, C Ruttkies, M Krauss, C Brouard, T Kind, K Dührkop | 3 | 2017 |
| 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 |
| 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 | 2 | 2016 |
| Protein-protein interaction network inference with semi-supervised Output Kernel Regression C Brouard, M Szafranski, F d'Alché-Buc JOBIM, 133-136, 2012 | 2 | 2012 |
| 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 | | 2020 |
| 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 | | 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 | | 2020 |
| Unsupervised variable selection for kernel methods in systems biology J Mariette, C Brouard, R Flamary, N Vialaneix Journée Régionale de Bioinformatique et Biostatistique, Génopole Toulouse, 2019 | | 2019 |
| Markov Logic Network for supervised gene regulation inference: application to the ID2 regulatory network in human keratinocytes C Brouard, J Dubois, C Vrain, D Castel, MA Debily, F d'Alché-Buc International Workshop on Machine Learning in Systems Biology, 2012 | | 2012 |
| A new theoretical angle to semi-supervised output kernel regression for protein-protein interaction network inference C Brouard, F d'Alché-Buc, M Szafranski International Workshop on Machine Learning in Systems Biology, 2011 | | 2011 |
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisVerified email at telecom-paris.fr
