Longitudinal big biological data in the AI era
Mardinoglu, Adil, Turkez, Hasan, Shong, Minho, Srinivasulu, Vishnuvardhan Pogunulu, Nielsen, Jens, Palsson, Bernhard O., Hood, Leroy, Uhlen, Mathias
Generating longitudinal and multi-layered big biological data is crucial for effectively implementing artificial intelligence (AI) and systems biology approaches in characterising whole-body biological functions in health and complex disease states. Big biological data consists of multi-omics, clinical, wearable device, and imaging data, and information on diet, drugs, toxins, and other environmental factors. Given the significant advancements in omics technologies, human metabologenomics, and computational capabilities, several multi-omics studies are underway. Here, we first review the recent application of AI and systems biology in integrating and interpreting multi-omics data, highlighting their contributions to the creation of digital twins and the discovery of novel biomarkers and drug targets. Next, we review the multi-omics datasets generated worldwide to reveal interactions across multiple biological layers of information over time, which enhance precision health and medicine. Finally, we address the need to incorporate big biological data into clinical practice, supporting the development of a clinical decision support system essential for AI-driven hospitals and creating the foundation for an AI and systems biology-based healthcare model.
- Journal
- MOLECULAR SYSTEMS BIOLOGY
- Year
- 2025
- Volume
- 21
- Issue
- 9
- Pages
- 1147-1165
- ISSN
- 1744-4292
- DOI
- 10.1038/s44320-025-00134-0
- WoS UID
- WOS:001544163200001