Stéphane Wenric

This study links genomic data with EHRs to identify health outcomes enriched in sub-populations, revealing both known and novel associations.

SpinAdapt is an unsupervised RNA correction algorithm that enables transfer of molecular models between labs without requiring patient-level data, improving reproducibility and privacy.

This study identifies genetic factors influencing blood pressure response to phenylephrine in perioperative settings, revealing population-specific differences and rare variants associated with non-response.

This study compares supervised learning methods to differential expression analysis for gene selection in RNA-Seq case-control studies, showing improved survival-associated gene identification in cancer datasets.

The paper analyzes natural antisense transcripts (NATs) in breast cancer using RNA sequencing to identify their potential role in gene regulation and survival outcomes.