E. Abbate, M. Boulakia, Y. Coudì-ere, J. Gerbeau, P. Zitoun et al., In silico assessment of the effects of various compounds in MEA/hiPSC-CM assays: Modeling and numerical simulations, Journal of Pharmacological and Toxicological Methods, vol.89
DOI : 10.1016/j.vascn.2017.10.005

N. M. Adams and D. J. Hand, Improving the Practice of Classifier Performance Assessment, Neural Computation, vol.37, issue.2, pp.305-311, 2000.
DOI : 10.1016/S0022-5347(01)66551-0

G. Cordeiro and . Thomas, Electrophysiological effects of ranolazine, a novel antianginal agent with antiarrhythmic properties, Circulation, vol.110, issue.8, pp.904-910, 2004.

S. Arikawa, S. Miyano, A. Shinohara, S. Kuhara, Y. Mukouchi et al., A machine discovery from amino acid sequences by decision trees over regular patterns, New Generation Computing, vol.2, issue.3-4, pp.361-375, 1993.
DOI : 10.1109/IJCNN.1990.137700

K. Blinova, J. Stohlman, J. Vicente, D. Chan, L. Johannesen et al., Comprehensive Translational Assessment of Human-Induced Pluripotent Stem Cell Derived Cardiomyocytes for Evaluating Drug-Induced Arrhythmias, Toxicological Sciences, vol.155, issue.1, pp.234-247, 2016.
DOI : 10.1093/toxsci/kfw200

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

D. Bottino, R. C. Penland, A. Stamps, M. Traebert, B. Dumotier et al., Preclinical cardiac safety assessment of pharmaceutical compounds using an integrated systems-based computer model of the heart, Progress in biophysics and molecular biology, pp.414-443, 2006.
DOI : 10.1016/j.pbiomolbio.2005.06.006

A. Bueno-orovio, E. M. Cherry, and F. H. Fenton, Minimal model for human ventricular action potentials in tissue, Journal of Theoretical Biology, vol.253, issue.3, pp.544-560, 2008.
DOI : 10.1016/j.jtbi.2008.03.029

I. Cavero, J. Guillon, V. Ballet, M. Clements, J. Gerbeau et al., Comprehensive in vitro Proarrhythmia Assay (C i PA): Pending issues for successful validation and implementation, Journal of Pharmacological and Toxicological Methods, vol.81, pp.21-36, 2016.
DOI : 10.1016/j.vascn.2016.05.012

C. Chang and C. Lin, LIBSVM, ACM transactions on intelligent systems and technology (TIST), p.27, 2011.
DOI : 10.1145/1961189.1961199

M. Clements and N. Thomas, High-Throughput Multi-Parameter Profiling of Electrophysiological Drug Effects in Human Embryonic Stem Cell Derived Cardiomyocytes Using Multi-Electrode Arrays, Toxicological Sciences, vol.140, issue.2, pp.445-461, 2014.
DOI : 10.1093/toxsci/kfu084

M. Clements and N. Thomas, High-Throughput Multi-Parameter Profiling of Electrophysiological Drug Effects in Human Embryonic Stem Cell Derived Cardiomyocytes Using Multi-Electrode Arrays, Toxicological Sciences, vol.140, issue.2, pp.445-461, 2014.
DOI : 10.1093/toxsci/kfu084

W. J. Crumb, J. Vicente, L. Johannesen, and D. G. Strauss, An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel, Journal of Pharmacological and Toxicological Methods, vol.81, pp.251-262, 2016.
DOI : 10.1016/j.vascn.2016.03.009

M. R. Davies, K. Wang, G. R. Mirams, A. Caruso, D. Noble et al., Recent developments in using mechanistic cardiac modelling for drug safety evaluation, Drug Discovery Today, vol.21, issue.6, 2016.
DOI : 10.1016/j.drudis.2016.02.003

B. Fermini, J. C. Hancox, N. Abi-gerges, M. Bridgland-taylor, K. W. Chaudhary et al., A New Perspective in the Field of Cardiac Safety Testing through the Comprehensive In Vitro Proarrhythmia Assay Paradigm, Journal of Biomolecular Screening, vol.134, issue.1, pp.1-11, 2016.
DOI : 10.1124/jpet.112.192609

. Woodward, Sundials: Suite of nonlinear and differential/algebraic equation solvers, ACM Transactions on Mathematical Software (TOMS), vol.31, issue.3, pp.363-396, 2005.

S. Hua and Z. Sun, A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach1 1Edited by B. Holland, Journal of Molecular Biology, vol.308, issue.2, pp.397-407, 2001.
DOI : 10.1006/jmbi.2001.4580

S. Kiranyaz, T. Ince, and M. Gabbouj, Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks, IEEE Transactions on Biomedical Engineering, vol.63, issue.3, pp.664-675, 2016.
DOI : 10.1109/TBME.2015.2468589

J. Kramer, C. A. Obejero-paz, G. Myatt, Y. A. Kuryshev, A. Bruening-wright et al., Mice models: superior to the herg model in predicting torsade de pointes Scientific reports, 2013.

M. C. Lancaster and E. Sobie, Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms, Clinical Pharmacology & Therapeutics, vol.2, issue.4, pp.371-379, 2016.
DOI : 10.1038/sj.bjp.0706070

E. Matsa, D. Rajamohan, E. Dick, L. Young, I. Mellor et al., Drug evaluation in cardiomyocytes derived from human induced pluripotent stem cells carrying a long QT syndrome type 2 mutation, European Heart Journal, vol.32, issue.8, pp.32952-962, 2011.
DOI : 10.1093/eurheartj/ehr073

T. Meyer, K. Boven, E. Günther, and M. Fejtl, Micro-Electrode Arrays in Cardiac Safety Pharmacology, Drug Safety, vol.13, issue.2, pp.763-772, 2004.
DOI : 10.2165/00002018-200427110-00002

G. Mirams, Y. Cui, A. Sher, M. Fink, J. Cooper et al., Simulation of multiple ion channel block provides improved early prediction of compounds??? clinical torsadogenic risk, Cardiovascular Research, vol.91, issue.1, pp.53-61, 2011.
DOI : 10.1093/cvr/cvr044

E. G. Navarrete, P. Liang, F. Lan, V. Sanchez-freire, C. Simmons et al., Screening Drug-Induced Arrhythmia Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes and Low-Impedance Microelectrode Arrays, Circulation, vol.128, issue.11_suppl_1, pp.128-131, 2013.
DOI : 10.1161/CIRCULATIONAHA.112.000570

URL : http://circ.ahajournals.org/content/circulationaha/128/11_suppl_1/S3.full.pdf

B. O. Donoghue and E. Candes, Adaptive restart for accelerated gradient schemes. Foundations of computational mathematics, pp.715-732, 2015.

F. Raphel, M. Boulakia, N. Zemzemi, Y. Coudì-ere, J. Guillon et al., Identification of ion currents components generating field potential recorded in MEA from hiPSC-CM, IEEE Transactions on Biomedical Engineering, 2017.
DOI : 10.1109/TBME.2017.2748798

URL : https://hal.archives-ouvertes.fr/hal-01570341

B. Schölkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, 2002.

C. W. Scott, M. F. Peters, and Y. P. Dragan, Human induced pluripotent stem cells and their use in drug discovery for toxicity testing, Toxicology Letters, vol.219, issue.1, pp.49-58, 2013.
DOI : 10.1016/j.toxlet.2013.02.020

M. Systems, Microelectrode array (mea) manual. http://www.multichannelsystems.com/sites/ multichannelsystems.com/files

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

L. Tung, A bi-domain model for describing ischemic myocardial D-C potentials, 1978.

N. Zemzemi, M. Bernabeu, J. Saiz, J. Cooper, P. Pathmanathan et al., Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials, British Journal of Pharmacology, vol.89, issue.3, pp.718-733, 2013.
DOI : 10.1161/01.CIR.93.3.407

URL : https://hal.archives-ouvertes.fr/hal-00764102