Céline Brouard
Céline Brouard
INRAE, MIA Toulouse, France
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
Liquid-chromatography retention order prediction for metabolite identification
E Bach, S Szedmak, C Brouard, S Böcker, J Rousu
Bioinformatics 34 (17), i875-i883, 2018
Magnitude-preserving ranking for structured outputs
C Brouard, E Bach, S Böcker, J Rousu
Asian Conference on Machine Learning, 407-422, 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), 1-14, 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
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
Pushing data into cp models using graphical model learning and solving
C Brouard, S Givry, T Schiex
International Conference on Principles and Practice of Constraint …, 2020
Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique
C Brouard
Université d'Évry-Val-d'Essonne, 2013
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
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
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
Regularized output kernel regression applied to protein-protein interaction network inference
C Brouard, M Szafranski, F d’Alché-Buc
NIPS MLCB Workshop, 2010
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
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
Protein-protein interaction network inference with semi-supervised Output Kernel Regression
C Brouard, M Szafranski, F d'Alché-Buc
JOBIM, 133-136, 2012
Feature selection for kernel methods in systems biology
C Brouard, J Mariette, R Flamary, N Vialaneix
NAR genomics and bioinformatics 4 (1), lqac014, 2022
Réseaux de neurones pour graphe pour la prédiction de phénotypes
N Vialaneix, C Brouard, P Vismara
Mars, 2021
The system can't perform the operation now. Try again later.
Articles 1–20