Promoting the sharing, discovering, and reuse of phenotypes by developing an ontology-driven phenotype library

Presentation Time: 04:15 PM - 04:30 PM

Abstract Keywords: Reproducibility, Knowledge Representation and Information Modeling, Fairness and Elimination of Bias
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Observational data from Electronic Health Records (EHRs) offer immense potential for clinical research, relying on the precise identification of patients with specific medical conditions, facilitated by phenotype definitions. our study designs an information model for standardized phenotype representation and implements it in a computable format. We establish a centralized repository for enhanced discoverability and develop a user-friendly web portal adhering to FAIR principles, enabling easy searching, downloading, and sharing of standardized phenotype definitions. we developed a unified ontology schema, Phenotype Definition Ontology (PDO) to consistently represent these diverse phenotypes information. The PDO serves as the cornerstone for representing essential phenotype information and supporting various phenotypes collection, curation, sharing, and reuse. We integrated 4 sources of publicly available phenotype definitions. In total, 3,542 phenotype definitions were successfully converted and represented using the unified PDO model and computable formats in our phenotype library. Guided by the PDO framework and adhering to FAIR principles, ComPLy serves as a centralized repository for phenotype sharing, offering the following key services, including Search, Browse, Download, Submit, and API services. Lastly, we self-assessed the FAIRness of our phenotype library resources.

Speaker(s):
Na Hong, PhD
Yale University

Author(s):
Na Hong, PhD - Yale University; Xubing Hao; Yujia Zhou, Ms - Yale University; Ryan Denlinger, PhD - Yale University; Yan Hu - UTHealth Science Center Houston; Xueqing Peng, PhD - Yale University; Fongci Lin, PhD - Yale University; Licong Cui, PhD - The University of Texas Health Science Center at Houston (UTHealth Houston) School of Biomedical Informatics; Yong Chen, PhD - University of Pennsylvania; Hua Xu, Ph.D - Yale University;