H. Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review, Journal of Computational Biology, vol.9, issue.1, pp.67-103507, 2002.
DOI : 10.1089/10665270252833208
URL : https://hal.archives-ouvertes.fr/inria-00072606

N. Friedman, M. Linial, I. Nachman, and D. Peer, Using Bayesian Networks to Analyze Expression Data, J Comp Bio, vol.7, pp.3-4601, 2000.

E. Segal, M. Shapira, A. Regev, D. Pe-'er, D. Botstein et al., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nature Genetics, vol.291, issue.2, pp.166-76, 2003.
DOI : 10.1038/35057062

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988.

F. Jensen, Bayesian Networks and Decision Graphs, 2001.
DOI : 10.1007/978-0-387-68282-2

P. Spirtes, C. Glymour, R. Scheines, S. Kauffman, V. Aimale et al., Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data, Proc. of the Atlantic Symposium on Computational Biology, 2000.

G. Imoto and M. , ESTIMATION OF GENETIC NETWORKS AND FUNCTIONAL STRUCTURES BETWEEN GENES BY USING BAYESIAN NETWORKS AND NONPARAMETRIC REGRESSION, Biocomputing 2002, pp.175-186, 2002.
DOI : 10.1142/9789812799623_0017

D. Pe-'er, A. Regev, G. Elidan, and N. Friedman, Inferring subnetworks from perturbed expression profiles, Bioinformatics, vol.17, issue.Suppl 1, pp.215-224, 2001.
DOI : 10.1093/bioinformatics/17.suppl_1.S215

A. Hartemink, D. Gifford, T. Jaakkola, and R. Young, USING GRAPHICAL MODELS AND GENOMIC EXPRESSION DATA TO STATISTICALLY VALIDATE MODELS OF GENETIC REGULATORY NETWORKS, Biocomputing 2001, pp.422-433, 2001.
DOI : 10.1142/9789814447362_0042

D. Husmeier, Reverse engineering of genetic networks with Bayesian networks, Biochemical Society Transactions, vol.31, issue.6, pp.1516-1518, 2003.
DOI : 10.1042/bst0311516

J. Pena, J. Bjorkegren, and J. Tegner, Growing Bayesian network models of gene networks from seed genes, Bioinformatics, vol.21, issue.Suppl 2, pp.224-229, 2005.
DOI : 10.1093/bioinformatics/bti1137

W. Buntine, A guide to the literature on learning probabilistic networks from data, IEEE Transactions on Knowledge and Data Engineering, vol.8, issue.2, pp.195-210, 1996.
DOI : 10.1109/69.494161

R. Robinson, Counting unlabeled acyclic digraphs, In Lecture Notes in Mathematics, vol.9, 1977.
DOI : 10.1016/S0021-9800(70)80089-8

. Chickering, Optimal Structure identification with greedy search, Journal of machine learning research, vol.3, pp.507-554, 2002.

G. Cooper and E. Herskovits, A Bayesian method for the induction of probabilistic networks from data, Machine Learning, vol.72, issue.4, pp.309-347, 1992.
DOI : 10.1007/BF00994110

D. Chickering, D. Heckermann, D. Fisher, D. Lenz, and H. , Learning bayesian networks is NP-complete In Learning from data: AI and Statistics, pp.121-130

K. Friedman, Being Bayesian About Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks, Machine Learning, vol.50, issue.1/2, pp.95-126, 2003.
DOI : 10.1023/A:1020249912095

T. Kocka and R. Castelo, Improved learning of Bayesian networks, UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, pp.269-276

M. Wong, W. Lam, and K. Leung, Using evolutionary programming and minimum description length principle for data mining of Bayesian networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.2, pp.174-178, 1999.
DOI : 10.1109/34.748825

P. Le, A. Bahl, and L. Ungar, Using prior knowledge to improve genetic network reconstruction from microarray data, Silico Biology, vol.4, issue.3, pp.335-53, 2004.

N. Friedman, Inferring Cellular Networks Using Probabilistic Graphical Models, Science, vol.303, issue.5659, pp.799-805, 2004.
DOI : 10.1126/science.1094068

J. Holland, Adaptation in Natural and Artificial Systems University of Michigan Press, 1975.

D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1989.

W. Hsu, H. Guo, B. Perry, and J. Stilson, A Permutation Genetic Algorithm For Variable Ordering In Learning Bayesian Networks From Data, GECCO '02: Proceedings of the Genetic and Evolutionary Computation Conference, pp.383-390

P. Larranaga, C. Kuijpers, R. Murga, and Y. Yurramendi, Learning Bayesian network structures by searching for the best ordering with genetic algorithms, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.26, issue.4, 1996.
DOI : 10.1109/3468.508827

P. Larranaga, M. Poza, Y. Yurramendi, R. Murga, and C. Kuijpers, Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.9, pp.912-926, 1996.
DOI : 10.1109/34.537345

R. Etxeberria, P. Larranaga, and J. Picaza, Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data. Pattern Recogn Lett, pp.11-131269, 1997.

J. Myers, K. Laskey, and K. Dejong, Learning Bayesian Networks from Incomplete Data using Evolutionary Algorithms, pp.458-465, 1999.

C. Cotta and J. Muruzabal, Towards a More Efficient Evolutionary Induction of Bayesian Networks, PPSN VII: Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, pp.730-739

C. Cotta and J. Troya, Analyzing Directed Acyclic Graph Recombination, Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications, pp.739-748, 2001.
DOI : 10.1007/3-540-45493-4_72

S. Mahfoud, Niching methods for genetic algorithms, 1995.

K. Jong, An analysis of the behavior of a class of genetic adaptive systems, 1975.

D. Chickering, A Transformational Characterization of Equivalent Bayesian Network Structures, Proceedings of the 11th Annual Conference on Uncertainty in Artificial Intelligence (UAI-95), pp.87-98

T. Verma and J. Pearl, Equivalence and Synthesis of Causal Models, 1991.

B. Schölkopf, A. Smola, K. Müller, M. Gerstner, . Germond et al., Kernel principal component analysis, 7th International Conference on Artificial Neural Networks, ICANN 97, pp.583-588
DOI : 10.1007/BFb0020217

T. Gärtner, K. Driessens, and J. Ramon, Graph Kernels and Gaussian Processes for Relational Reinforcement Learning, Thirteenth International Conference on Inductive Logic Programming, 2003.
DOI : 10.1007/978-3-540-39917-9_11

J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. Liu, Learning Bayesian networks from data: An information-theory based approach, Artificial Intelligence, vol.137, issue.1-2
DOI : 10.1016/S0004-3702(02)00191-1

I. Tsamardinos, L. Brown, and C. Aliferis, The max-min hill-climbing Bayesian network structure learning algorithm, Machine Learning, vol.9, issue.2/3, pp.31-78, 2006.
DOI : 10.1007/s10994-006-6889-7

D. Heckerman and D. Geiger, Likelihoods and parameter priors for Bayesian networks, 1995.

C. Aliferis, I. Tsamardinos, A. Statnikov, and L. Brown, Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery, pp.371-376, 2003.

M. Quach and N. Brunel, Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference, Bioinformatics, vol.23, issue.23, pp.3209-3216, 2007.
DOI : 10.1093/bioinformatics/btm510
URL : https://hal.archives-ouvertes.fr/hal-00341950

P. Giudici and R. Castelo, Improving Markov Chain Monte Carlo model search for Data Mining, Machine Learning, vol.50, issue.1/2, pp.127-158, 2003.
DOI : 10.1023/A:1020202028934