| Dataset’s chemical diversity limits the generalizability of machine learning predictions M Glavatskikh, J Leguy, G Hunault, T Cauchy, B Da Mota Journal of Cheminformatics 11 (1), 69, 2019 | 91 | 2019 |
| EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation J Leguy, T Cauchy, M Glavatskikh, B Duval, B Da Mota Journal of cheminformatics 12, 1-19, 2020 | 52 | 2020 |
| Robust regression for large-scale neuroimaging studies V Fritsch, B Da Mota, E Loth, G Varoquaux, T Banaschewski, GJ Barker, ... NeuroImage 111, 431-441, 2015 | 24 | 2015 |
| Generic Machine Learning Pattern for Neuroimaging-Genetic Studies in the Cloud B Da Mota, R Tudoran, A Costan, G Varoquaux, G Brasche, P Conrod, ... Frontiers in Neuroinformatics 8, 31, 2014 | 16 | 2014 |
| Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization J Leguy, M Glavatskikh, T Cauchy, B Da Mota Journal of cheminformatics 13, 1-17, 2021 | 15 | 2021 |
| Randomized parcellation based inference B Da Mota, V Fritsch, G Varoquaux, T Banaschewski, GJ Barker, ... NeuroImage 89, 203-215, 2014 | 15 | 2014 |
| A-Brain: Using the Cloud to Understand the Impact of Genetic Variability on the Brain RT G Antoniu, A Costan, B Da Mota, B Thirion ERCIM News 2012 (89), 2012 | 14* | 2012 |
| Quchemreport: A python program for control quality and automatic generation of quantum chemistry results T Cauchy, B Da Mota University of Angers, Angers, France, 2020 | 8* | 2020 |
| A new parallel architecture for QBF tools B Da Mota, P Nicolas, I Stéphan High Performance Computing and Simulation (HPCS), 2010 International …, 2010 | 8 | 2010 |
| From (quantified) boolean formulae to answer set programming I Stéphan, B Da Mota, P Nicolas Journal of logic and computation 19 (4), 565-590, 2008 | 8 | 2008 |
| Enhancing the Reproducibility of Group Analysis with Randomized Brain Parcellations B Da Mota, V Fritsch, G Varoquaux, V Frouin, JB Poline, B Thirion Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013, 591-598, 2013 | 6 | 2013 |
| A fast computational framework for genome-wide association studies with neuroimaging data B Da Mota, V Frouin, E Duchesnay, S Laguitton, G Varoquaux, JB Poline, ... 20th International Conference on Computational Statistics (COMPSTAT 2012), 2012 | 6 | 2012 |
| Une nouvelle stratégie de mise sous forme prénexe pour des formules booléennes quantifiées avec bi-implications B Da Mota, I Stéphan, P Nicolas Revue internationale I 3, 9-29, 2009 | 4 | 2009 |
| A unified framework for Certificate and Compilation for QBF I Stéphan, B Da Mota Indian Conference on Logic and Its Applications, 210-223, 2009 | 3 | 2009 |
| Base littérale et certificat pour les formules booléennes quantifiées I Stéphan, B Da Mota JFPC 2008-Quatrièmes Journées Francophones de Programmation par Contraintes …, 2008 | 3 | 2008 |
| Machine learning in prediction of DFT-derived geometries and energies. M Glavatskikh, J Leguy, B da Mota, T Cauchy Journées Scientifiques SCF-BPL 2019, 2019 | 2 | 2019 |
| Des réseaux de neurones pour prédire des distances interatomiques extraites d’une base de données ouverte de calculs en chimie quantique J Leguy, T Cauchy, B Duval, B Da Mota Extraction et Gestion des Connaissances: Actes de la conférence EGC'2019 79, 2019 | 2 | 2019 |
| Prédire une géométrie moléculaire convergée par des modèles d’apprentissage automatique T Cauchy, B da Mota Workshop «L’intelligence artificielle pour la chimie des matériaux», 2018 | 2 | 2018 |
| Definition and exploration of realistic chemical spaces using the connectivity and cyclic features of ChEMBL and ZINC T Cauchy, J Leguy, B Da Mota Digital Discovery 2 (3), 736-747, 2023 | 1 | 2023 |
| Robust Group-Level Inference in Neuroimaging Genetic Studies V Fritsch, B Da Mota, G Varoquaux, V Frouin, E Loth, JB Poline, B Thirion 2013 International Workshop on Pattern Recognition in Neuroimaging, 21-24, 2013 | 1 | 2013 |
Thomas CauchyUniversité d'AngersVerified email at univ-angers.fr
