F. Nassir, R. S. Rector, G. M. Hammoud, and J. A. Ibdah, Pathogenesis and Prevention of Hepatic Steatosis, Gastroenterol. Hepatol. (N Y), vol.11, pp.167-175, 2015.

G. Bedogni, V. Nobili, and C. Tiribelli, Epidemiology of fatty liver: an update, World J. Gastroenterol, vol.20, pp.9050-9054, 2014.

C. E. Foulds, L. S. Trevino, B. York, and C. L. Walker, Endocrine-disrupting chemicals and fatty liver disease, Nat. Rev. Endocrinol, vol.2017, pp.445-457

S. A. Polyzos, J. Kountouras, G. Deretzi, C. Zavos, and C. S. Mantzoros, The emerging role of endocrine disruptors in pathogenesis of insulin resistance: a concept implicating nonalcoholic fatty liver disease, Curr. Mol. Med, vol.12, pp.68-82, 2012.

O. Yang, H. L. Kim, J. I. Weon, and Y. R. Seo, Endocrine-disrupting Chemicals: Review of Toxicological Mechanisms Using Molecular Pathway Analysis, J. Cancer Prev, vol.20, pp.12-24, 2015.

R. Benigni, C. L. Battistelli, C. Bossa, A. Giuliani, and O. Tcheremenskaia, Endocrine Disruptors: Data-based survey of in vivo tests, predictive models and the Adverse Outcome Pathway, Regul. Toxicol. Pharmacol, vol.86, pp.18-24, 2017.

J. C. Dearden, The History and Development of Quantitative Structure-Activity Relationships (QSARs). IJQSPR, vol.1, p.43, 2016.

Y. Low, T. Uehara, Y. Minowa, H. Yamada, Y. Ohno et al., Predicting druginduced hepatotoxicity using QSAR and toxicogenomics approaches, Chem. Res. Toxicol, vol.24, pp.1251-1262, 2011.

K. E. Tollefsen, S. Scholz, M. T. Cronin, S. W. Edwards, J. De-knecht et al., Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA), Regul. Toxicol. Pharmacol, vol.70, pp.629-640, 2014.

M. Leist, A. Ghallab, R. Graepel, R. Marchan, R. Hassan et al., Arch. Toxicol, vol.91, pp.3477-3505, 2017.

G. Patlewicz, T. W. Simon, J. C. Rowlands, R. A. Budinsky, and R. A. Becker, Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes, Regul. Toxicol. Pharmacol, vol.71, pp.463-477, 2015.

G. T. Ankley, R. S. Bennett, R. J. Erickson, D. J. Hoff, M. W. Hornung et al., Adverse Outcome Pathways: a Conceptual Framework to Support Ecotoxicology Research and Risk Assessment, Environ. Toxicol. Chem, vol.29, pp.730-741, 2010.

J. Strickland, Q. Zang, M. Paris, D. M. Lehmann, D. Allen et al., Multivariate models for prediction of human skin sensitization hazard, J. Appl. Toxicol, vol.37, pp.347-360, 2017.

M. Daneshian, H. Kamp, J. Hengstler, M. Leist, and B. Van-de-water, Highlight report: Launch of a large integrated European in vitro toxicology project: EU-ToxRisk, Arch. Toxicol, vol.90, pp.1021-1024, 2016.

C. Wittwehr, H. Aladjov, G. Ankley, H. J. Byrne, J. De-knecht et al., How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology, Toxicol. Sci, vol.155, issue.17, pp.5-12, 2007.

K. Mansouri, A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha et al., CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ. Health Perspect, vol.124, pp.1023-1033, 2016.

S. Boughorbel, F. Jarray, and M. El-anbari, Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric, PLoS One, vol.12, 2017.

R. Huang and M. Xia, Editorial: Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways As Mediated by Exposure to Environmental Toxicants and Drugs. Front, Environ. Sci, issue.5, 2017.

L. Y. Lee, U. A. Kohler, L. Zhang, D. Roenneburg, S. Werner et al., Activation of the Nrf2-ARE pathway in hepatocytes protects against steatosis in nutritionally induced non-alcoholic steatohepatitis in mice, EPA. Toxicity ForeCaster (ToxCast?) Data, vol.142, issue.23, pp.361-374, 2014.

S. Romanov, A. Medvedev, M. Gambarian, N. Poltoratskaya, M. Moeser et al., Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors, Nat. Methods, vol.5, pp.253-260, 2008.

M. T. Martin, D. J. Dix, R. S. Judson, R. J. Kavlock, D. M. Reif et al., Impact of environmental chemicals on key transcription regulators and correlation to toxicity end points within EPA's ToxCast program, Chem. Res. Toxicol, vol.23, issue.27, pp.578-590, 2010.

M. Floris, A. Manganaro, O. Nicolotti, R. Medda, G. F. Mangiatordi et al., A generalizable definition of chemical similarity for read-across, J. Cheminform, 2017.

R. Judson, K. Houck, M. Martin, A. M. Richard, T. B. Knudsen et al., Editor's Highlight: Analysis of the Effects of, Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space. Toxicol. Sci, vol.152, pp.323-339, 2016.

B. W. Matthews, Comparison of the predicted and observed secondary structure of T4 phage lysozyme, Biochim. Biophys. Acta, vol.405, pp.442-451, 1975.

J. A. Cooper, R. Saracci, and P. Cole, Describing the validity of carcinogen screening tests, Br. J. Cancer, vol.39, pp.87-89, 1979.

J. A. Hanley and B. J. Mcneil, A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology, vol.148, pp.839-882, 1983.

P. Gramatica, Principles of QSAR models validation: internal and external, QSAR Comb. Sci, vol.26, pp.694-701, 2007.

L. Eriksson, J. Jaworska, A. P. Worth, M. T. Cronin, R. M. Mcdowell et al., Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs, Environ. Health Perspect, vol.111, issue.36, pp.5-32, 2001.

R. Bryll, R. Gutierrez-osuna, F. Quek, A. V. Zakharov, M. L. Peach et al., Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets, Data Analysis, vol.36, pp.319-326, 2003.

C. Chen and A. Liaw, Using Random Forest to Learn Imbalanced Data, 2004.

A. Liaw, M. Wiener, T. I. Netzeva, A. Worth, T. Aldenberg et al., Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52, ATLA, Altern. Lab. Anim, vol.2, issue.43, pp.155-173, 2002.

R. P. Sheridan, Three useful dimensions for domain applicability in QSAR models using random forest, J. Chem. Inf. Model, vol.52, pp.814-823, 2012.

D. Gadaleta, N. Porta, E. Vrontaki, S. Manganelli, A. Manganaro et al., Integrating computational methods to predict mutagenicity of aromatic azo compounds, J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev, vol.35, issue.46, pp.239-257, 2017.

Z. Li, M. Berk, T. M. Mcintyre, G. J. Gores, and A. E. Feldstein, The LysosomalMitochondrial Axis in Free Fatty Acid-Induced Hepatic Lipotoxicity, Hepatology, vol.47, pp.1495-1503, 2008.

J. F. Truchon, C. I. Bayly, N. Triballeau, F. Acher, I. Brabet et al., Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4, J. Chem. Inf. Model, vol.47, issue.49, pp.2534-2547, 2005.

P. G. Polishchuk, E. N. Muratov, A. G. Artemenko, O. G. Kolumbin, N. N. Muratov et al., Application of random forest approach to QSAR prediction of aquatic toxicity, J. Chem. Inf. Model, vol.49, pp.2481-2488, 2009.

V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan et al., Random forest: a classification and regression tool for compound classification and QSAR modeling, J. Chem. Inf. Comput. Sci, vol.43, pp.1947-1958, 2003.

X. W. Zhu, Y. J. Xin, and Q. H. Chen, Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests, SAR QSAR Environ. Res, vol.27, pp.559-572, 2016.

T. M. Martin, Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering, SAR QSAR Environ. Res, vol.27, pp.17-30, 2016.

O. Soufan, W. Ba-alawi, M. Afeef, M. Essack, V. Rodionov et al., Mining Chemical Activity Status from High-Throughput Screening Assays, PLoS One, vol.10, p.144426, 2015.

R. Huang, M. Xia, D. Nguyen, T. Zhao, S. Sakamuru et al., Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs, Front. Environ. Sci, p.3, 2016.

L. Han, Y. Wang, S. H. Bryant, P. Mazzatorta, L. A. Tran et al., Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem, J. Chem. Inf. Model, vol.9, issue.57, pp.34-42, 2007.

A. Tropsha, Best Practices for QSAR Model Development, Validation, and Exploitation, Mol. Inf, vol.29, pp.476-488, 2010.

W. P. Walters, M. T. Stahl, and M. A. Murcko, Virtual screening-an overview, Drug Discov. Today, vol.3, pp.160-178, 1998.

O. Oecd, Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure-Activity Relationship Models, 2018.

F. Y. Bois, Bayesian statistical inference for SBML-coded systems biology models, Bioinformatics, vol.25, pp.1453-1454, 2009.
URL : https://hal.archives-ouvertes.fr/ineris-00961935

S. J. Park, O. A. Ogunseitan, and R. P. Lejano, Dempster-Shafer theory applied to regulatory decision process for selecting safer alternatives to toxic chemicals in consumer products, Integr. Environ. Assess. Manag, vol.10, pp.12-21, 2014.