An Ontology-Driven Approach to Integrating Clinical and Genomic Data for Precision Medicine Applications
Keywords:
Biomedical Ontologies, Clinical Data Integration, Genomic Data, Semantic Interoperability, Knowledge Representation, Precision Medicine, Ontology Alignment, Electronic Health Records, Translational Bioinformatics, Data HarmonizationAbstract
The integration of clinical and genomic data holds transformative potential for precision medicine, enabling tailored therapeutic strategies that reflect individual patient variability. Despite the proliferation of big data in biomedicine, the challenge remains in harmonizing heterogeneous datasets derived from electronic health records (EHRs), laboratory results, and high-throughput sequencing technologies. An ontology-driven approach offers a structured semantic framework to unify diverse datasets and facilitate meaningful data interpretation. This study explores an ontology-based architecture that leverages domain-specific ontologies to enable seamless data integration, interoperability, and advanced reasoning for decision support in precision healthcare. Using real-world datasets and open biomedical ontologies, we demonstrate enhanced phenotype-genotype correlations and improved diagnostic classifications. The proposed methodology underscores the feasibility of ontological systems to bridge semantic gaps between data modalities, paving the way for scalable, explainable, and clinically relevant applications. The findings suggest that integrating ontologies into biomedical informatics not only augments data reuse and discovery but also supports robust clinical decision-making.
References
Luciano, J. S., Andersson, B., & Batchelor, C. (2011). The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside. Journal of Biomedical Semantics.
Venkata Sambasivarao Kopparapu. Cloud-Integrated Artificial Intelligence Framework for MRI Analysis: Advancing Radiological Diagnostics Through Automated Solutions. International Journal of Computer Engineering and Technology (IJCET), 16(1), 2025, 2892-2907. doi: https://doi.org/10.34218/IJCET_16_01_203
Brochhausen, M., Spear, A. D., Cocos, C., & Weiler, G. (2011). The ACGT Master Ontology and its applications–Towards an ontology-driven cancer research and management system. Journal of Biomedical Informatics.
Hsu, W., Gonzalez, N. R., & Chien, A. (2015). An integrated, ontology-driven approach to constructing observational databases for research. Journal of Biomedical Informatics.
Kamdar, M. R., Fernández, J. D., & Polleres, A. (2019). Enabling web-scale data integration in biomedicine through linked open data. NPJ Digital Medicine.
Venkata Sambasivarao Kopparapu. (2025). Healthcare Insurance Data Infrastructure: A Comprehensive Analysis of EDI Standards and Processing Systems. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 8(1), 2341-2353. doi: https://doi.org/10.34218/IJRCAIT_08_01_170
Silva, M. C., Eugénio, P., Faria, D., & Pesquita, C. (2022). Ontologies and knowledge graphs in oncology research. Cancers.
Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., & Dennis, S. (2013). Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature. International Journal of Medical Informatics.
Legaz-García, M. C., Miñarro-Giménez, J. A., & García-Gómez, J. M. (2016). Generation of open biomedical datasets through ontology-driven transformation and integration processes. Journal of Biomedical Semantics.
Guarino, N., & Welty, C. A. (2002). Evaluating ontological decisions with OntoClean. Communications of the ACM.
Baader, F., Horrocks, I., & Sattler, U. (2010). Description Logics as ontology languages for the Semantic Web. In Reasoning Web.
Wang, L. L. (2019). Ontology-driven pathway data integration. University of Washington.
Sfakianakis, S., & Blazantonakis, M. (2010). Decision support based on genomics: integration of data-and knowledge-driven reasoning. International Journal of Bioinformatics and Biomedical Engineering.
Tradigo, G., Veneziano, C., Greco, S., & Veltri, P. (2014). An architecture for integrating genetic and clinical data. Procedia Computer Science.
Bharambe, U., & Narvekar, C. (2021). Ontological Perspective on Cancer Care and Genomic Data Integration. In Data Science with Semantic Technologies.
He, Y., et al. (2020). Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project. Nature Reviews Nephrology.
Liaw, S. T., Taggart, J., Yu, H., & de Lusignan, S. (2014). Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease classification. Journal of Biomedical Informatics.
