Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies. - Raphaël Mourad; Christine Sinoquet; Philippe Leray 001 (COMPSTAT, 2010)

A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies. - Raphaël Mourad; Christine Sinoquet; Philippe Leray 001 (BMC Bioinform., 2011)

Modélisation pangénomique du déséquilibre de liaison à l'aide de réseaux bayésiens hiérarchiques latents et applications. (Genome-wide linkage disequilibrium modeling using hierarchical latent Bayesian networks and applications). - Raphaël Mourad (2011)

Forests of Latent Tree Models for the Detection of Genetic Associations. - Christine Sinoquet; Raphaël Mourad; Philippe Leray 001 (BIOINFORMATICS, 2012)

Forests of Latent Tree Models to Decipher Genotype-Phenotype Associations. - Christine Sinoquet; Raphaël Mourad; Philippe Leray 001 (BIOSTEC (Selected Papers), 2012)

Probabilistic graphical models for genetic association studies. - Raphaël Mourad; Christine Sinoquet; Philippe Leray 001 (Briefings Bioinform., 2012)

A dynamic time order network for time-series gene expression data analysis. - Pengyue Zhang; Raphaël Mourad; Yang Xiang; Kun Huang 001; Tim Hui-Ming Huang; Kenneth P. Nephew; Yunlong Liu; Lang Li 001 (BMC Syst. Biol., 2012)

A Survey on Latent Tree Models and Applications. - Raphaël Mourad; Christine Sinoquet; Nevin Lianwen Zhang; Tengfei Liu; Philippe Leray 001 (J. Artif. Intell. Res., 2013)

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation. - Raphaël Mourad; Olivier Cuvier (PLoS Comput. Biol., 2016)

Uncovering direct and indirect molecular determinants of chromatin loops using a computational integrative approach. - Raphaël Mourad; Lang Li 001; Olivier Cuvier (PLoS Comput. Biol., 2017)

Studying 3D genome evolution using genomic sequence. - Raphaël Mourad (Bioinform., 2020)

DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions. - Vincent Rocher; Matthieu Genais; Elissar Nassereddine; Raphaël Mourad (PLoS Comput. Biol., 2021)

Data analyses and bioinformatic models for genomics. (Analyses de données et modèles bioinformatiques pour la génomique). - Raphaël Mourad (2022)

TADreg: a versatile regression framework for TAD identification, differential analysis and rearranged 3D genome prediction. - Raphaël Mourad (BMC Bioinform., 2022)

Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences. - Raphaël Mourad (BMC Bioinform., 2023)

Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction. - Han Phan; Céline Brouard; Raphaël Mourad (Briefings Bioinform., 2024)

Should we really use graph neural networks for transcriptomic prediction? - Céline Brouard; Raphaël Mourad; Nathalie Vialaneix (Briefings Bioinform., 2024)