Alzheimer's Disease Diagnostics Using miRNA Biomarkers and Machine Learning.

Background: The current standard for Alzheimer's disease (AD) diagnosis is often imprecise, as with memory tests, and invasive or expensive, as with brain scans. However, the dysregulation patterns of miRNA in blood hold potential as useful biomarkers for the non-invasive diagnosis and even treatment of AD.

Objective: The goal of this research is to elucidate new miRNA biomarkers and create a machine-learning (ML) model for the diagnosis of AD.

Methods: We utilized pathways and target gene networks related to confirmed miRNA biomarkers in AD diagnosis and created multiple models to use for diagnostics based on the significant differences among miRNA expression between blood profiles (serum and plasma).

Results: The best performing serum-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 92.0% accuracy and the best performing plasma-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 90.9% accuracy. Through analysis of AD implicated miRNA, thousands of descriptors reliant on target gene and pathways were created which can then be used to identify novel biomarkers and strengthen disease diagnosis.

Conclusion: Development of a ML model including miRNA and their genomic and pathway descriptors made it possible to achieve considerable accuracy for the prediction of AD.