Florence d'Alché-Buc
Florence d'Alché-Buc
Institut Mines-Télécom / Télécom ParisTech
Verified email at telecom-paristech.fr - Homepage
TitleCited byYear
Gene networks inference using dynamic Bayesian networks
BE Perrin, L Ralaivola, A Mazurie, S Bottani, J Mallet, F d’Alche–Buc
Bioinformatics 19 (suppl_2), ii138-ii148, 2003
Estimating parameters and hidden variables in nonlinear state-space models based on ODEs for biological networks
M. Quach, N Brunel, F d'Alché-Buc
Bioinformatics 23 (23), 3209-3216, 2007
Support vector machines based on a semantic kernel for text categorization
G Siolas, F d'Alché-Buc
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural …, 2000
Incremental support vector machine learning: A local approach
L Ralaivola, F d’Alché-Buc
International Conference on Artificial Neural Networks, 322-330, 2001
Semi-supervised marginboost
F d'Alché-Buc, Y Grandvalet, C Ambroise
Advances in neural information processing systems, 553-560, 2001
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
Dynamical modeling with kernels for nonlinear time series prediction
F d'Alche-Buc, L Ralaivola
Advances in neural information processing systems 16, 129, 2004
Kernelizing the output of tree-based methods
P Geurts, L Wehenkel, F d'Alché-Buc
Proceedings of the 23rd international conference on Machine learning, 345-352, 2006
Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues
G Michailidis, F d’Alché-Buc
Mathematical biosciences 246 (2), 326-334, 2013
Time series filtering, smoothing and learning using the kernel Kalman filter
L Ralaivola, F d'Alché-Buc
Proceedings. 2005 IEEE International Joint Conference on Neural Networks …, 2005
Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset
C Auliac, V Frouin, X Gidrol, F d'Alché-Buc
BMC bioinformatics 9 (1), 91, 2008
Inferring biological networks with output kernel trees
P Geurts, N Touleimat, M Dutreix, F d'Alché-Buc
BMC bioinformatics 8 (2), S4, 2007
System monitoring the discharging period of the charging/discharging cycles of a rechargeable battery, and host device including a smart battery
JN Patillon, F D'Alche-Buc, JP Nadal
US Patent 5,936,385, 1999
RAR/RXR binding dynamics distinguish pluripotency from differentiation associated cis-regulatory elements
B Chatagnon, Veber, Morin, Bedo, Triqueneaux, Sémon, Laudet, d'Alché-Buc
Nucleic Acids Research 43 (10), 4833-4854, 2015
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 (176), 1-48, 2016
Operator-valued kernel-based vector autoregressive models for network inference
N Lim, F d’Alché-Buc, C Auliac, G Michailidis
Machine learning 99 (3), 489-513, 2015
OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks
N Lim, Y Şenbabaoğlu, G Michailidis, F d’Alché-Buc
Bioinformatics 29 (11), 1416-1423, 2013
CycSim—an online tool for exploring and experimenting with genome-scale metabolic models
F Le Fèvre, S Smidtas, C Combe, M Durot, F d'Alché-Buc, V Schachter
Bioinformatics 25 (15), 1987-1988, 2009
Trio learning: a new strategy for building hybrid neural trees
F D’ALCHÉ-BUC, D Zwierski, JP Nadal
International journal of neural systems 5 (04), 259-274, 1994
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