Jean−Fred Fontaine

GenesetDiseaseDrugNetwork (GDDN): a web server for disease enrichment and drug prioritization

GDDN connects gene-sets to diseases and therapeutics to prioritize drugs, enabling functional analysis and personalized medicine.

Interpretable machine learning models for single-cell ChIP-seq imputation

SIMPA imputes missing regions in scChIP-seq data using machine learning models trained on bulk data, improving clustering and pathway gene identification while enabling model interpretation.

Identification of functional residues using machine learning provides insights into the evolution of odorant receptor gene families in solitary and social insects

The paper uses machine learning to identify functional residues in odorant receptors across insect species, revealing conservation patterns linked to social behavior.

Automated quality control of next generation sequencing data using machine learning

This paper develops machine learning models for automated quality control of next generation sequencing data, validated on internal and external datasets.

Computational identification of cell-specific variable regions in ChIP-seq data

The paper develops a method to identify cell-specific variable regions in ChIP-seq data by integrating replicated experiments, finding regions with variable occupancy that may have functional significance.

TAF-ChIP: An ultra-low input approach for genome wide chromatin immunoprecipitation assay

TAF-ChIP enables high-quality ChIP-Seq results from low cell numbers, matching conventional ChIP-Seq datasets in epigenetic profiles.