Kévin Yauy

Clinical Evaluation of an AI System for Streamlined Variant Interpretation in Genetic Testing

DiagAI, a machine-learning system, improves variant interpretation in genetic testing by prioritizing causal variants, outperforming existing tools with higher diagnostic yield and shorter variant shortlists.

A genome-wide, machine learning-guided exploration of the cis-regulatory code involved in neuronal differentiation

The paper develops a machine learning method to identify cis-regulatory element profiles and their sequence features during neuronal differentiation using single-cell ATAC-seq and RNA-seq data.

Assessing feasibility and risk to translate, de-identify and summarize medical reports using deep learning

This paper evaluates deep learning methods for translating, de-identifying, and summarizing non-English medical reports, finding non-inferior performance in de-identification and high accuracy in summarization.

Exome sequencing as a first-tier test for copy number variant detection : retrospective evaluation and prospective screening in 2418 cases.

This paper evaluates exome sequencing as an alternative to chromosomal microarray for detecting copy number variants in rare disorders, showing improved diagnostic yield in prospective cases.

Learning phenotypic patterns in genetic disease by symptom interaction modeling

This paper develops a symptom interaction model to address heterogeneous phenotype reporting in genetic disease, improving gene prioritization by 42%.

Genome Alert!: a standardized procedure for genomic variant reinterpretation and automated genotype-phenotype reassessment in clinical routine

Genome Alert! automatically detects changes in variant classifications and gene-disease associations over time, improving clinical interpretation of genomic data with minimal human effort.