Decoding Aging with AI and Computational Modeling
A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cell Biology and Pathology".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 58
Special Issue Editors
Interests: cancer; fibroblast; exosomes; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Interests: bioinformatics; omics; AI; aging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Aging is a multifactorial, dynamic process that spans from molecular and cellular scales to systemic physiology, manifesting as cumulative damage, compensatory remodeling, and altered resilience. Recent advances in artificial intelligence (AI), machine learning, and systems modeling now enable us to quantify, simulate, and predict aging trajectories in ways that were previously unattainable. This Special Issue invites contributions that leverage computational and AI-driven approaches to deepen mechanistic insight into aging, improve predictive accuracy, and suggest intervention strategies.
We welcome original research and review papers in areas including (but not limited to) the following:
- AI-based modeling of molecular aging processes (e.g., transcriptomics, epigenetics, proteostasis);
- Multiscale and hybrid models bridging cellular, tissue, and organismal aging;
- Longitudinal trajectory prediction of healthspan, biological age, and frailty;
- Generative modeling and synthetic aging simulation (e.g., imaging, tissue dynamics);
- Explainable/interpretable AI approaches to aging biomarkers;
- Intervention modeling and in silico experiments (e.g., for senolytics, caloric restriction);
- Integration of multimodal data (omics, imaging, wearables) in aging modeling;
- Ethical, reliability, and reproducibility considerations in AI models of aging,
By bringing together computational, biological, and biomedical perspectives, this Special Issue aims to promote cross-disciplinary dialogue and accelerate the development of robust AI tools that can predict, explain, and ultimately modulate the aging process.
Dr. Bingrui Li
Dr. Xuanye Cao
Dr. Wen Jiang
Guest Editors
Manuscript Submission Information
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Keywords
- aging
- computational modeling
- AI
- healthspan and lifespan prediction
- systems biology
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