@Article{cmes.2026.079390,
AUTHOR = {Roshana Mukhtar, Chuan-Yu Chang, Muhammad Asif Zahoor Raja},
TITLE = {Machine Learning Knowledge Driven Nonlinear Autoregressive Exogenous Networks for Fractional Order Proteasome-Fibril Interaction Model in Parkinson’s Disease Dynamics},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/26582},
ISSN = {1526-1506},
ABSTRACT = {Parkinson’s disease (PD) is a complex neurodegenerative disease associated with the accumulation of α-synuclein, which is linked to the dysfunctional ubiquitin–proteasome system. Fractional calculus has emerged as a powerful tool for modeling complex disease dynamics due to its promising features that inherently capture memory and hereditary effects. This paper presents a fractional-order Proteasome-Fibril interaction model (F-PFIM) for the dynamics of PD, represented by three fractional differential classes, showing concentrations of fibrils (F), proteasomes (P), and proteasome fibril complex (C). The three classes of the F-PFIM collectively make a controlling system that works for the clearance of unnecessary protein from the cell to maintain cell hemostasis. When the P levels are very low, and F accumulation is high, the cell degradation machinery becomes overburdened. The prolonged instability of accumulated proteins leads to slow and progressive neurodegeneration associated with the onset of PD. Machine learning knowledge-driven nonlinear autoregressive exogenous networks backpropagated with Levenberg-Marquardt optimization (NAREN-LM) are presented to analyze the temporal evolution dynamics of F, C, and P in F-PFIM for different fractional orders varying from (0.89, 0.90, …, 1). The reference dataset is generated through the fractional Adams method (FAM) and is given to NAREN-LM in the form of training, testing, and validation sets. The performance of NAREN-LM is verified by analyzing the solution dynamics of F-PFIM in terms of mean square error-based convergence curves for training and testing, histogram plots, regression, and correlation results. Furthermore, the comparison of the NAREN-LM solution dynamics and corresponding absolute errors with those of the FAM endorses the accuracy of machine learning knowledge-driven predictive networks for F-PFIM.},
DOI = {10.32604/cmes.2026.079390}
}