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A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data
Daichi Shigemizu1,2,3,4, Shintaro Akiyama5, Yuya Asanomi5
1Laboratory Chief, Division of Genomic Medicine, Medical Genome Center, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan. d.shigemizu@gmail.com.
Background:
Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers.
Methods:
In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree.
Results:
The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021).
Conclusions:
Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.
Insights
Researchers developed a machine learning model using serum microRNAs (miRNAs) to predict Dementia with Lewy bodies (DLB). The model achieved high accuracy, identifying BCL2L1 and PIK3R2 as potential biomarkers for DLB.
Area of Science:
- Neuroscience
- Biomarker Discovery
- Computational Biology
Background:
- Dementia with Lewy bodies (DLB) is a common neurodegenerative disease, second only to Alzheimer's disease.
- Current diagnostic methods for DLB have limitations in sensitivity.
- MicroRNAs (miRNAs) are emerging as promising biomarkers for early detection.
Purpose of the Study:
- To identify potential serum miRNA biomarkers for prodromal Dementia with Lewy bodies (DLB).
- To construct an accurate risk prediction model for DLB using machine learning.
- To investigate the association of miRNA target genes with DLB pathology.
Main Methods:
- Serum miRNA expression data from 478 Japanese individuals were analyzed.
- Machine learning algorithms including penalized regression, random forest, support vector machine, and gradient boosting decision tree were employed.
- A risk prediction model was built using 180 miRNAs and two clinical features.
Main Results:
- The gradient boosting decision tree model achieved an accuracy of 0.829 on an independent test set.
- Gene set enrichment analysis identified 6 functional genes in the DHA signaling pathway linked to DLB.
- BCL2L1 and PIK3R2 showed statistically significant associations with DLB through gene-based association studies.
Conclusions:
- The developed prediction model serves as an effective tool for DLB classification.
- BCL2L1 and PIK3R2 are identified as statistically significant genetic risk factors for DLB.
