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Related Experiment Video

Updated: Dec 7, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Phenotypically Similar Rare Disease Identification from an Integrative Knowledge Graph for Data Harmonization:

Qian Zhu¹, Dac-Trung Nguyen¹, Gioconda Alyea²

¹Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States.

JMIR Medical Informatics

|October 2, 2020

Summary

This study identifies phenotypically similar rare diseases to improve data harmonization. The findings enhance consistency across rare disease resources, supporting translational research and clinical decision-making.

Keywords:

GARD data harmonization phenotypical similarity rare diseases

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Area of Science:

Genomics and Bioinformatics
Medical Informatics
Rare Disease Research

Background:

Standardized protocols for rare disease data harmonization are lacking.
Existing resources exhibit data redundancy and inconsistency.
This hinders clinical decision-making and education in rare diseases.

Purpose of the Study:

To systematically identify phenotypically similar Genetic and Rare Diseases (GARD).
To support rare disease data harmonization through knowledge graph analysis.
To determine similarity types among GARD diseases.

Main Methods:

Programmatic identification of phenotypically similar GARD diseases.
Comparison of disease mappings between GARD and other rare disease resources.
Derivation of clinical manifestations from disease classifications and prioritization based on phenotypes and genotypes.

Main Results:

Validated 87% of identified phenotypically similar disease pairs.
Achieved 94% precision and 86% F-measure in similarity identification.
Identified 662 phenotypically similar disease pairs for GARD data harmonization.

Conclusions:

Successfully identified phenotypically similar rare diseases using two distinct approaches.
Results will guide GARD data harmonization and expand translational science.
Enhanced data transparency and consistency across rare disease resources.