ArticleBMC medical genomics2019
A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer's disease created by integrative analysis of multi-omics data.Alzheimer's research & therapy · 2020Pooled it
- Plasma multi-miRNA models classify Alzheimer's, Parkinson's, and Lewy body dementia.Frontiers in aging neuroscience · 2026Article
- miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.International journal of molecular sciences · 2025Article
- Alzheimer's disease may develop from changes in the immune system, cell cycle, and protein processing following alterations in ribosome function.Scientific reports · 2025Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs.BMC bioinformatics · 2024Article
- Article
- Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia.Frontiers in bioengineering and biotechnology · 2023Article
- MicroRNA-22-3p ameliorates Alzheimer's disease by targeting SOX9 through the NF-κB signaling pathway in the hippocampus.Journal of neuroinflammation · 2022Article
- MicroRNA Networks in Cognition and Dementia.Cells · 2022Review
- Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores.Journal of personalized medicine · 2022Article
- Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.Frontiers in genetics · 2022Article
- Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases.Frontiers in molecular neuroscience · 2022Review
- Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data.Scientific reports · 2021Article
- Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters.Biomedicines · 2021Article
- Genetic Architecture and Molecular, Imaging and Prodromic Markers in Dementia with Lewy Bodies: State of the Art, Opportunities and Challenges.International journal of molecular sciences · 2021Review
- Unearthing of Key Genes Driving the Pathogenesis of Alzheimer's Disease via Bioinformatics.Frontiers in genetics · 2021Article
- Identification of a Qualitative Signature for the Diagnosis of Dementia With Lewy Bodies.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDementia 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.
methodsIn 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.
resultsThe 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).
conclusionsOur 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.
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