Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning

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.

We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers.

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.

The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.