Language
English
Publication Date
2-3-2026
Journal
ArXiv
PMID
41675350
PMCID
PMC12889852
PubMedCentral® Posted Date
2-3-2026
PubMedCentral® Full Text Version
Pre-Print
Abstract
Millions of children worldwide are affected by severe rare Mendelian disorders, yet exome and genome sequencing still fail to provide a definitive molecular diagnosis for a large fraction of patients, prolonging the diagnostic odyssey. Bridging this gap increasingly requires transitioning from DNA-only interpretation to multi-modal diagnostic reasoning that combines genomic data, transcriptomic sequencing (RNA-seq), and phenotype information; however, computational frameworks that coherently integrate these signals remain limited. Here we present RareCollab, an agentic diagnostic framework that pairs a stable quantitative Diagnostic Engine with Large Language Model (LLM)-based specialist modules that produce high-resolution, interpretable assessments from transcriptomic signals, phenotypes, variant databases, and the literature to prioritize potential diagnostic variants. In a rigorously curated benchmark of Undiagnosed Diseases Network (UDN) patients with paired genomic and transcriptomic data, RareCollab achieved 77% top-5 diagnostic accuracy and improved top-1 to top-5 accuracy by ~20% over widely used variant-prioritization approaches. RareCollab illustrates how modular artificial intelligence (AI) can operationalize multi-modal evidence for accurate, scalable rare disease diagnosis, offering a promising path toward reducing the diagnostic odyssey for affected families.
Published Open-Access
yes
Recommended Citation
Qi, Guantong; Wang, Jiasheng; Chong, Mei Ling; et al., "RareCollab -- An Agentic System Diagnosing Mendelian Disorders with Integrated Phenotypic and Molecular Evidence" (2026). Faculty, Staff and Students Publications. 8372.
https://digitalcommons.library.tmc.edu/baylor_docs/8372