Survey and improvement strategies for gene prioritization with large language models.

Motivation: Rare diseases remain difficult to diagnose due to limited patient data and genetic diversity, with many cases remaining undiagnosed despite advances in variant prioritization tools. While large language models have shown promise in medical applications, their optimal application for trustworthy and accurate gene prioritization downstream of modern prioritization tools has not been systematically evaluated.

Results: We benchmarked various language models for gene prioritization using multi-agent and Human Phenotype Ontology classification approaches to categorize patient cases by phenotype-based solvability levels.

The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing.

RNA sequencing (RNA-seq) has recently been used in translational research settings to facilitate diagnoses of Mendelian disorders. A significant obstacle for clinical laboratories in adopting RNA-seq is the low or absent expression of a significant number of disease-associated genes/transcripts in clinically accessible samples. As this is especially problematic in neurological diseases, we developed a clinical diagnostic approach that enhanced the detection and evaluation of tissue-specific genes/transcripts through fibroblast-to-neuron cell transdifferentiation.