Comparative Screening of Alzheimer's Disease, Lewy Body Dementia, and Frontotemporal Dementia Using miRNA and Machine
Benjamin Luo1, Valentina L Kouznetsova2,3,4, Igor F Tsigelny3,4,5
1REHS program of San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA.
Abstract:
Current dementia diagnostic methods can be costly, invasive, or limited in their ability to distinguish between disorders with overlapping clinical symptoms. Dysregulated microRNAs (miRNAs) have emerged as promising noninvasive biomarkers for neurodegenerative disease, but individual miRNA changes alone may not capture the complex molecular patterns needed for accurate disease classification. Machine learning provides a way to integrate multiple layers of miRNA-derived information and identify disease-specific biomarker signatures. In this study, we developed machine learning models to classify dysregulated miRNAs associated with Alzheimer's disease dementia (AD), Lewy body dementia (LBD), and frontotemporal dementia (FTD). Each miRNA was represented using sequence-derived descriptors, predicted gene targets, and KEGG pathway features. The highest-performing models trained on AD, LBD, and FTD achieved 10-fold cross-validation accuracies of 90.6%, 92.9%, and 100%, respectively. When evaluated on independent datasets, the AD, LBD, and FTD models achieved accuracies of 88.9%, 77.8%, and 90.9%, respectively. Cross-disease testing showed reduced performance when models were applied across dementia types, suggesting partially disease-specific miRNA patterns while also indicating overlap among the molecular signatures of AD, LBD, and FTD. These results suggest that machine learning-based integration of miRNA sequence, target-gene, and pathway information can improve the identification of dementia-associated biomarker signatures and may support the future development of noninvasive diagnostic tools for dementia.
Insights
Machine learning models accurately identify dementia types using microRNA biomarkers. This approach integrates miRNA sequence, gene targets, and pathways for improved noninvasive diagnostic tools.
Area of Science:
- Biomarkers and diagnostics
- Neuroscience
- Bioinformatics
Background:
- Current dementia diagnostics are often invasive, costly, and struggle with overlapping symptoms.
- MicroRNAs (miRNAs) show potential as noninvasive biomarkers for neurodegenerative diseases.
- Individual miRNA changes may not be sufficient for accurate dementia classification.
Purpose of the Study:
- To develop machine learning models for classifying Alzheimer's disease dementia (AD), Lewy body dementia (LBD), and frontotemporal dementia (FTD) using miRNA data.
- To integrate diverse miRNA-derived information, including sequence, gene targets, and pathway features.
- To assess the diagnostic accuracy and disease specificity of machine learning models for dementia subtypes.
Main Methods:
- Machine learning models were trained to classify AD, LBD, and FTD based on miRNA features.
- Features included sequence-derived descriptors, predicted gene targets, and KEGG pathway information.
- Models were evaluated using 10-fold cross-validation and independent test datasets, including cross-disease testing.
Main Results:
- High cross-validation accuracies were achieved: 90.6% for AD, 92.9% for LBD, and 100% for FTD.
- Independent dataset accuracies were 88.9% for AD, 77.8% for LBD, and 90.9% for FTD.
- Cross-disease testing revealed reduced performance, indicating partially disease-specific yet overlapping miRNA patterns.
Conclusions:
- Machine learning effectively integrates multi-layered miRNA information for dementia classification.
- This approach enhances the identification of dementia-specific biomarker signatures.
- The findings support the development of noninvasive diagnostic tools for differentiating dementia subtypes.
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