Summary

BMC Med Genomics. 2019 Oct 30;12(1):150. doi: 10.1186/s12920-019-0607-3.

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

Abstract:

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.

日本語要旨:

microRNA発現プロファイルデータから、レビー小体型認知症(DLB)の判別モデルを機械学習を適応することで構築した

PMID:  31666070

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