#AI reads Urine# Ontology-guided clustering enables proteomic analysis of rare pediatric disorders

This study addresses the challenge of drawing meaningful biological conclusions from rare pediatric disorders due to small patient numbers by developing a framework that integrates clinical ontologies with proteomic analysis. It analyzed urine and plasma samples from 1140 children and adolescents, covering 394 distinct diseases and healthy controls. Using advanced mass spectrometry techniques, over 5000 proteins in urine, 900 proteins in undepleted plasma, and 1900 proteins in perchloric acid-depleted plasma were quantified. By embedding SNOMED CT clinical terminology into a network structure, rare diseases were grouped based on their clinical relationships, enabling statistical analysis even for diseases with as few as two patients. This approach revealed molecular signatures across different developmental stages and disease clusters, while taking into account age- and sex-specific variations, providing a generalizable solution for studying heterogeneous patient populations and bridging the gap between clinical classification and molecular analysis of rare diseases.

EMBO Mol Med. 2025 Jul;17(7):1842-1867. doi: 10.1038/s44321-025-00253-z.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/