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Updated: May 22, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
Published on: August 20, 2019
Identifying Phenotypes for Earlier Diagnosis of Rare Diseases
Casey N Ta1, Cong Liu1,2, Chunhua Weng1
1Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, U.S.A.
Abstract:
Rare diseases, while individually rare, cumulatively affect a large population, and patients often undergo long and arduous diagnostic odysseys. Toward the goal of supporting earlier diagnosis of rare diseases, we developed generalizable methods of extracting rare diseases and phenotypes from structured electronic health records and clinical notes. We analyzed the distributions of the age of onset of phenotypes per disease to identify disease-phenotype associations, producing a dataset with over 500 thousand associations covering 2300 rare diseases. Disease-phenotype associations are characterized by disease prevalence and mean age of onset of the phenotype to aid phenotype selection according to the priorities of the clinical decision support task.
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