Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.

Alzheimer's disease is a complex disorder of the nervous system. Diagnosing this disease is a costly process in which numerous laboratory tests and examinations are conducted. Most computational methods for Alzheimer's disease prediction face low accuracy due to challenges such as a limited number of training samples, noisy/overlapping data, and variability in gene expression. This study presents a reliable computational approach for predicting Alzheimer's disease through a new method of analyzing gene expression profiles from either brain tissue or blood samples. The proposed Petri net-based approach demonstrates superior diagnostic accuracy compared to existing methods across multiple gene expression datasets derived from both blood and brain tissue. The proposed method runs a Petri net model of the signaling pathways involved in complex nervous system disorders. In addition, the Petri net model provides step-by-step tracking of gene activation until the final diagnosis state is reached. An accurate understanding of the functions of the key genes of the signaling pathways involved in brain cell death will play a significant role in the early diagnosis of this complex disease and hopefully will lead to the identification of suitable preventive treatments or drug targets.