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Structure: Internal structure identifier : 537409
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Réseaux Bayésiens Dynamiques : méthodologie pour l’inférence sur les schémas de mode d’Action en Toxicologie

Frédéric Y. Bois , Wang Gao , Ghislaine Gayraud
9. Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes (JFRB 2018), May 2018, Toulouse, France. pp.47-56
Conference papers ineris-01863861v1

Quantitative modelling of adverse outcome pathway for risk assessment

Wang Gao , Ghislaine Gayraud , Frédéric Y. Bois
18. Journée du réseau AIGM "Algorithmic Issues for Inference in Graphical Models", Dec 2017, Toulouse, France
Conference papers ineris-01853594v1
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A quantitative AOP of mitochondrial toxicity based on data from three cell lines

Cleo Tebby , Wang Gao , Johannes Delp , Giada Carta , Wanda van der Stel , et al.
Toxicology in Vitro, 2022, 81, pp.105345. ⟨10.1016/j.tiv.2022.105345⟩
Journal articles ineris-03742099v1
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Application of three approaches for quantitative AOP development to renal toxicity

Elias Zgheib , Wang Gao , Alice Limonciel , Hristo Aladjov , Huan Yang , et al.
Computational Toxicology, 2019, 11, pp.1-13. ⟨10.1016/j.comtox.2019.02.001⟩
Journal articles hal-02987394v1
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Probabilistic generation of random networks taking into account information on motifs occurrence

Frédéric Y. Bois , Ghislaine Gayraud
Journal of Computational Biology, 2015, 22 (1), pp.25-36. ⟨10.1089/cmb.2014.0175⟩
Journal articles ineris-01862569v1

Graph_sampler : a simple tool for fully Bayesian analyses of DAG-models

Sagnik Datta , Ghislaine Gayraud , Eric Leclerc , Frédéric Y. Bois
Computational Statistics, 2017, 32, pp.691-716. ⟨10.1007/s00180-017-0719-1⟩
Journal articles ineris-01853448v1
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Structure learning of Bayesian networks involving cyclic structures

Witold Wiecek , Frédéric Bois , Ghislaine Gayraud
Preprints, Working Papers, ... hal-02130362v1