Artificial Intelligence-Driven Multi-Omics Diagnostic Pipelines for Infectious, Neurodegenerative, and Metabolic Diseases: From Biomarker Discovery to Precision Medicine and Digital Twin Healthcare
Emmanuel Nkansah1, Micheal Abimbola Oladosu2, Moses Adondua Abah3, Olaide Ayokunmi Oladosu4
1Emmanuel Nkansah, Department of Accounting, Economics and Finance, School of Business, La Sierra University, Riverside, United States.
2Micheal Abimbola Oladosu, Department of Chemical Sciences, Faculty of Sciences, Anchor University, Ayobo, Ipaja, Lagos, Nigeria.
3Moses Adondua Abah, Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University of Wukari, Wukari, Taraba State, Nigeria.
4Olaide Ayokunmi Oladosu, Department of Computer Science, Faculty of Science and Technology, Babcock University, Ilishan-Remo, Nigeria.
Manuscript received on 12 June 2026 | First Revised Manuscript received on 23 June 2026 | Second Revised Manuscript received on 28 June 2026 | Manuscript Accepted on 15 July 2026 | Manuscript published on 30 July 2026 | PP: 26-33 | Volume-6 Issue-5 July 2026 | Retrieval Number: 100.1/ijpmh.E116206050726 | DOI: 10.54105/ijpmh.E1162.06050726
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The combination of AI and multi-omics has ushered in a new, revolutionary era in disease diagnosis and precision medicine. This review aims to summarize the current status of the artificial intelligence (AI)-based multi-omics diagnostic workflows employed in three disease paradigms: infectious, neurodegenerative, and metabolic diseases, and critically evaluate their translational potential from the discovery of biomarkers to digital twin healthcare systems. Machine Learning (ML) and Deep Learning (DL) algorithms, as well as Explainable Artificial Intelligence (XAI) algorithms, are explored for their application in linking genomics, transcriptomics, proteomics, metabolomics, and microbiomics datasets to enable high-dimensional molecular phenotyping. Harmonisation strategies for multi-omics data, AIdriven feature selection, molecular pathway elucidation using graph neural networks (GNNs) and transformer architectures, and the development of digital twin models for personalised, dynamic health simulation are among the key themes. We delve deeper into the regulatory, ethical, and equity issues arising from the deployment of AI-omics systems across heterogeneous clinical settings. The review ends with a strategy for integrating validated AI-omics pipelines into the next-generation precision therapeutics and global health infrastructure.
Keywords: Artificial Intelligence; Multi-Omics; Biomarker Discovery; Precision Medicine; Digital Twin; Infectious Diseases; Neurodegenerative Diseases; Metabolic Diseases; Machine Learning; Deep Learning.
Scope of the Article: Health Care Management