Michael A. Gargano

A Phenotypic Paradigm for Cerebral Palsy Genetics

The paper identifies pathogenic variants in 15.8% of CP patients and finds only 16 of 60 genes have significant CP association.

Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease

This paper introduces a genomic framework to prioritize mouse strains for preclinical disease modeling, showing high sensitivity in predicting strain vulnerability to diseases.

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools

This study benchmarks large language models against a traditional diagnostic tool for genetic disease diagnosis using 5,213 case reports, finding LLMs lag behind in accuracy.

Leveraging Generative AI to Accelerate Biocuration of Medical Actions for Rare Disease

AutoMAxO uses LLMs to semi-automate MAxO curation for rare diseases, identifying 538 confirmed disease-treatment annotations from 4,918 medical abstracts.

The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

The paper introduces the GA4GH Phenopacket schema to standardize the exchange of phenotypic data for precision medicine applications.

Interpretable prioritization of splice variants in diagnostic next-generation sequencing

SQUIRLS prioritizes splice variants by calculating interpretable features and achieving higher accuracy and speed than existing methods in simulated exomes.

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

The paper introduces LIRICAL, a likelihood ratio-based framework for genomic diagnostics that improves accuracy and interpretability in phenotype-driven rare disease diagnosis.