Integrating Multi-Omics and Personalized Medicine in One Health: A Systems Biology Approach to Precision Healthcare

Abstract

The integration of multi-omics technologies with personalized medicine within a One Health framework signifies a paradigm shift in healthcare and disease management. By leveraging systems biology approaches, researchers can unravel the complex relationship between environmental health and humans and animals, paving the way for precision healthcare solutions. This review examines the convergence of multi-omics (genomics, transcriptomics, proteomics, metabolomics, and microbiomics) with personalized medicine, highlighting its significance in enhancing diagnostics, therapeutic strategies, and public health interventions. To our knowledge, this review is among the first to thoroughly synthesize multi-omics technologies, personalized medicine, and the One Health framework through a systems biology perspective, offering an integrative view that connects human, animal, and environmental health in the context of precision psychiatry and global health innovation. We systematically examine recent advancements in data integration, computational modeling, and artificial intelligence (AI)-augmented analytics for multi-omics datasets. We emphasize significant applications in oncology, infectious diseases, antibiotic resistance, and chronic conditions to illustrate the translational significance of our methodology. The integration of multi-omics in One Health has significantly improved biomarker identification, therapeutic optimization, and early disease detection, particularly in oncology, infectious disease epidemiology, and antimicrobial stewardship. Nonetheless, challenges persist regarding data harmonization, ethical considerations, and regulatory frameworks. A systems biology-based, multi-omics approach for personalized medicine holds substantial promise for precision healthcare. Addressing computational, ethical, and translational limits will be vital in attaining its full potential. Future research should focus on developing scalable AI-driven data integration frameworks, enhancing bioinformatics pipelines, and standardizing policies to support sustainable healthcare improvements within the One Health paradigm.

Received: 2 April 2025 | Revised: 18 August 2025 | Accepted: 30 September 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available and accessible upon responsible request to the corresponding author.

Author Contribution Statement

Onyeka Mary Ukpoju-Ebonyi: Methodology, Writing – original draft. Jean-Marie Akor Ebonyi: Conceptualization. Taiwo Temitope Ogunjobi: Conceptualization, Investigation, Writing – review & editing. Daniel Ebubechi Obasi: Writing - original draft. Daniel Chukwuemeka Ugwu: Writing – review & editing. Goodluck Oluchukwu Anyanwu: Investigation, Supervision. Stella Opeyemi Abolade: Methodology, Project administration.