Deep learning-based approaches for cellular mechanics analysis and secure data sharing in biomechanics
Year: 2025
Type: article
Abstract: Cellular mechanics behavior, encompassing properties such as elasticity, viscosity, and stress-strain responses, is fundamental to understanding disease mechanisms, tissue regeneration, and drug development. This study proposes a deep learning-based framework integrating Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and federated learning to model and analyze cellular mechanics while enabling secure data sharing. The proposed methods preserve critical biomechanical insights, such as force-displacement curves and cellular deformation patterns, while mitigating re-identification risks during multi-institutional collaborations. Experimental evaluations demonstrate the framework’s effectiveness in maintaining data utility and analytical accuracy, paving the way for advancing biomechanics research and fostering applications in regenerative medicine and tissue engineering.
DOI: 10.62617/mcb1059
Authors: Jing Huang, Tao Duan
Institution: Henan University of Traditional Chinese Medicine
Language: English
Subfield: Cell Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4408007368