Advancing a biomechanical framework for cellular interaction and mechanotransduction analysis using data mining techniques

As biomechanics advances, understanding the structural and functional dynamics of biological systems at molecular and cellular levels becomes increasingly critical. This study investigates a biomechanical framework for analyzing cellular interaction forces and information flow in mechanotransduction processes. Leveraging Data Mining (DM) technology, we evaluate cellular system efficiency and adaptation mechanisms under varying biomechanical stimuli, simulating stress conditions akin to high parallel processing loads. The proposed algorithm achieves an accuracy of 95.27% in predicting cellular response behaviors, with system stability maintained above 90% under simulated mechanical stress. These findings highlight the potential of computational methods to enhance our understanding of cellular resilience and adaptation in biomechanical contexts. Ultimately, this work contributes to the development of robust, adaptive models for studying the mechanobiology of cells and tissues, advancing diagnostic and therapeutic approaches in molecular and cellular biomechanics.