Aligning Orphanet Classification to Identify Disease Characteristics among Rare Disease Clusters

Understanding the underlying etiologies of rare diseases may facilitate research across multiple conditions, enabling basket trail design and drug repurposing. In this study, we aligned clusters of rare diseases with Orphanet classifications to represent their shared etiologies and establish a foundation for further investigation on underly biological mechanism discovery. By utilizing the linearized Orphanet categories, we connected 35 clusters of rare diseases into 18 classifications. Significant associations were found between the categories "Rare Developmental Defects During Embryogenesis" and "Rare Inborn Errors of Metabolism" and the clusters in this study, suggesting that many rare diseases originating in the prenatal period or related to metabolism may present a substantial opportunity for success in future investigation.

Authors

Institutions

Publication Details

Published
2024-12-03
DOI
https://doi.org/10.1109/bibm62325.2024.10822379
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Aligning Orphanet Classification to Identify Disease Characteristics among Rare Disease Clusters

Sungrim Moon, Ewy A. Mathé, Qian Zhu, Jessica Maine
Genomics and Rare Diseases
article

Aligning Orphanet Classification to Identify Disease Characteristics among Rare Disease Clusters

Sungrim Moon, Ewy A. Mathé, Qian Zhu, Jessica Maine
article en

Abstract

Understanding the underlying etiologies of rare diseases may facilitate research across multiple conditions, enabling basket trail design and drug repurposing. In this study, we aligned clusters of rare diseases with Orphanet classifications to represent their shared etiologies and establish a foundation for further investigation on underly biological mechanism discovery. By utilizing the linearized Orphanet categories, we connected 35 clusters of rare diseases into 18 classifications. Significant associations were found between the categories "Rare Developmental Defects During Embryogenesis" and "Rare Inborn Errors of Metabolism" and the clusters in this study, suggesting that many rare diseases originating in the prenatal period or related to metabolism may present a substantial opportunity for success in future investigation.

Vol. 2024
National Institutes of Health (US), National Center for Advancing Translational Sciences (US)
National Institutes of Health, National Center for Advancing Translational Sciences
Zero hunger
Openalex Percentile: Top 73%
Genomics and Rare Diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.