Description

This research initiative employs a two-phase approach to optimize Alzheimer's disease (AD) detection and progression. The initial phase targets the identification of unique markers associated with apoptotic neuronal cell death in AD, highlighting Cytochrome C and caspases as potential candidates. While Cytochrome C shows promise as a neuronal cell death indicator, its certainty is constrained by a limited time frame. Conversely, the complexities surrounding the activation of caspases—particularly executioner caspases—introduce uncertainties due to discrepancies between their activation and actual cell death. In the second phase, the aim is to develop a predictive model using Artificial Intelligence (AI) methods, leveraging advancements in clinical bioinformatics to enable early detection and personalized treatment strategies. This research endeavor endeavors to optimize methods of AD detection and progression assessment utilizing AI-based predictive modeling via identifying the specific neuronal cell death marker in Phase I. The ultimate objective is to provide experts with sophisticated tools for more informed, faster decision-making in managing Alzheimer's disease.