Work

Aligning Orphanet Classification to Identify Disease Characteristics among Rare Disease Clusters

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

DOI: 10.1109/bibm62325.2024.10822379

Authors: Sungrim Moon, Jessica Maine, Ewy Mathe, Qian Zhu

Institutions: National Institutes of Health, National Center for Advancing Translational Sciences

Language: English

Topic: Genomics and Rare Diseases

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Center for Advancing Translational Sciences