Shahida Moosa

This paper identifies that truncating variants in KAT6A cause two distinct neurodevelopmental disorders with opposing gene regulatory and metabolic effects, highlighting the need for variant-level stratification in therapy development.

Bone2Gene uses deep learning to classify rare bone diseases from hand radiographs, achieving high accuracy for distinct disorders but lower for overlapping ones.

RareLink enables interoperability between REDCap and global health standards by exporting data to Phenopackets and FHIR, improving rare disease research scalability and data reuse.

This paper introduces GestaltGAN, a generative model that creates photorealistic portraits of individuals with rare genetic disorders while preserving privacy.

The paper curates 111 gene-disease relationships for syndromic disorders, classifying their clinical validity and identifying gaps in knowledge.

The paper uses next-generation phenotyping to identify Cornelia de Lange Syndrome in Nigerian children through phenotypic similarity and exome sequencing.

This study compares human and computer attention patterns when assessing images of genetic conditions using eye-tracking and deep learning saliency maps.

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

The paper evaluates a structured diagnostic framework for ultra-rare diseases, showing improved genetic diagnoses and novel gene-disease associations using exome sequencing and AI tools.

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.