Kristin M. Abbott

DAVE: how to use explainable AI to interpret missense variants for genome diagnostics based on functional protein modeling

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

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

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

Improving the diagnostic yield of exome-sequencing, by predicting gene-phenotype associations using large-scale gene expression analysis

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.

Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

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.