Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning.
Document Type
Article
Publication Date
1-12-2024
Publication Title
Front Biosci (Landmark Ed)
Keywords
Humans; Parkinson Disease; MicroRNAs; Deep Learning; Machine Learning; Biomarkers; california; pni
Abstract
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.
Clinical Institute
Neurosciences (Brain & Spine)
Department
Neurosciences
Recommended Citation
Kumar, Alex; Kouznetsova, Valentina L; Kesari, Santosh; and Tsigelny, Igor F, "Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning." (2024). Articles, Abstracts, and Reports. 8493.
https://digitalcommons.providence.org/publications/8493