K. Joeri van der Velde

The paper evaluates how natural genetic diversity affects the thermodynamic impact of pathogenic variants in small human proteins, showing significant background-dependent stability changes in most tested variants.

DAVE uses explainable AI to interpret missense variants by integrating biophysically grounded features, improving variant classification and interpretability for genome diagnostics.

The paper describes a unified data infrastructure for rare disease research, enabling collaborative analysis of multi-omics data across institutions.

The paper integrates DNA methylation and gene expression networks to improve prediction of functional gene-gene associations using eQTMs and a novel integration pipeline.

CAPICE is a machine learning method that prioritizes pathogenic variants in exomes, outperforming existing tools for rare and ultra-rare variants.

This paper introduces GADO, a tool that uses gene expression data to predict gene-phenotype associations and improve diagnostic yield in exome sequencing by prioritizing variants in patients with unknown genetic diagnoses.

GAVIN classifies variants with high sensitivity and specificity using gene-specific calibrations, outperforming 12 other tools in a clinical benchmark.

The paper identifies genetic variants affecting gene expression using genotypes derived from public RNA-seq data, finding cis-eQTLs for 8,034 genes and strong ASE effects for 34 rare pathogenic variants.