Background: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD.
Methods: We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers.
Results: The best-performing ML model, trained on filtered miRNA dysregulated in PD, was able to identify miRNA biomarkers with 95.65% accuracy. Through analysis of miRNA implicated in PD, thousands of descriptors reliant on gene targets were created that can be used to identify novel biomarkers and strengthen PD diagnosis.
Conclusions: The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.
Keywords: Parkinson's disease; deep learning; machine learning; miRNA biomarkers; neural networks.
© 2024 The Author(s). Published by IMR Press.