A New N-Channel Maximum Entropy Method in NMR for Automatic Reconstruction of "Decoupled Spectra" and J-Coupling Determination. - Véronique Stoven; Jean-Philippe Annereau; Marc-André Delsuc; Jean Y. Lallemand (J. Chem. Inf. Comput. Sci., 1997)

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines. - Pierre Mahé; Liva Ralaivola; Véronique Stoven; Jean-Philippe Vert (J. Chem. Inf. Model., 2006)

Virtual screening of GPCRs: An in silico chemogenomics approach. - Laurent Jacob; Brice Hoffmann; Véronique Stoven; Jean-Philippe Vert (BMC Bioinform., 2008)

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction. - Brice Hoffmann; Mikhail Zaslavskiy; Jean-Philippe Vert; Véronique Stoven (BMC Bioinform., 2010)

Predicting drug side-effect profiles: a chemical fragment-based approach. - Edouard Pauwels; Véronique Stoven; Yoshihiro Yamanishi (BMC Bioinform., 2011)

Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions. - Yoshihiro Yamanishi; Edouard Pauwels; Hiroto Saigo; Véronique Stoven (J. Chem. Inf. Model., 2011)

Relating drug-protein interaction network with drug side effects. - Sayaka Mizutani; Edouard Pauwels; Véronique Stoven; Susumu Goto; Yoshihiro Yamanishi (Bioinform., 2012)

Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. - Yasuo Tabei; Edouard Pauwels; Véronique Stoven; Kazuhiro Takemoto; Yoshihiro Yamanishi (Bioinform., 2012)

Multitask Feature Selection with Task Descriptors. - Victor Bellon; Véronique Stoven; Chloé-Agathe Azencott (PSB, 2016)

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes. - Olivier Collier; Véronique Stoven; Jean-Philippe Vert (PLoS Comput. Biol., 2019)

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity. - Benoit Playe; Véronique Stoven (J. Cheminformatics, 2020)

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients. - Jarne Verhaeghe; Sofie A. M. Dhaese; Thomas De Corte; David Vander Mijnsbrugge; Heleen Aardema; Jan G. Zijlstra; Alain G. Verstraete; Véronique Stoven; Pieter Colin; Femke Ongenae; Jan J. De Waele; Sofie Van Hoecke (BMC Medical Informatics Decis. Mak., 2022)

Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset. - Gwenn Guichaoua; Philippe Pinel; Brice Hoffmann; Chloé-Agathe Azencott; Véronique Stoven (J. Chem. Inf. Model., 2024)