Denise Horn

The paper introduces the GestaltMatcher Database (GMDB), a global facial phenotypic reference for rare diseases, showing improved NGP performance with diverse ancestry data.

GestaltMatcher improves diagnosis of ultra-rare genetic disorders by matching facial phenotypes to molecular diagnoses using a deep learning model trained on a large dataset of patient photographs.

This paper introduces a CRISPR-Cas9-engineered mouse model for GPI-anchor deficiency that mirrors human phenotypes, including cognitive impairments and epilepsy, and identifies synaptic transmission deficits.

The paper introduces an AI-driven method that uses facial images to prioritize exome data, achieving 99% top-10 accuracy in identifying disease-causing genes.

The paper reports a new clinical syndrome linked to ACTB mutations, characterized by thrombocytopenia, microcephaly, and developmental disability, and identifies altered cytoskeletal interactions in affected cells.

The paper evaluates clinical features, flow cytometry, and facial image analysis to classify GPIBDs and identify gene-specific patterns, finding that facial recognition software is most effective for gene prediction.