Open Access Journal of Applied Science and Technology(OAJAST)

Department of Family Medicine, Dong-eui Medical Center, Yangjeong-ro, Busanjin-gu, Busan, Republic of Korea

Publications

• Research Article   
  Retro-Augmented Spatiotemporal Causal Decision Trees for Cellular Rejuvenation: A Mathematical Framework Integrating Reinforcement Learning, Renormalization Group Theory, and Partial Differential Causal Inference
  Author(s): Chur Chin*
  
  Cellular aging represents one of the most profound biological challenges of our time, characterized by progressive accumulation of epigenetic alterations, transcriptomic dysregulation, and attractor basin displacement in high- dimensional gene expression state spaces. We present a novel mathematical architecture—the Retro-Augmented Spatiotemporal Causal Decision Tree (RA-SCDT)—that unifies reinforcement learning, causal inference, renormalization group (RG) theory, entanglement entropy formalism, and locally stable partial differential equations (PDEs) to model and optimize cellular rejuvenation trajectories. The central innovation is the incorporation of future-state information (I_{t+1→t}) into past decision nodes via retro-augmented state definitions, enabling backward-propagated optimization of transcription factor interventions. We formalize the aging process as .. Read More»
  

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