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dc.contributor.authorShigemizu, Daichi
dc.contributor.authorAkiyama, Shintaro
dc.contributor.authorAsanomi, Yuya
dc.contributor.authorBoroevich, Keith A
dc.contributor.authorSharma, Alok
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorSakurai, Takashi
dc.contributor.authorOzaki, Kouichi
dc.contributor.authorOchiya, Takahiro
dc.contributor.authorNiida, Shumpei
dc.date.accessioned2020-01-31T00:48:09Z
dc.date.available2020-01-31T00:48:09Z
dc.date.issued2019
dc.identifier.issn1755-8794
dc.identifier.doi10.1186/s12920-019-0607-3
dc.identifier.urihttp://hdl.handle.net/10072/391013
dc.description.abstractBackground: 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBMC
dc.relation.ispartofissue1
dc.relation.ispartofjournalBMC Medical Genomics
dc.relation.ispartofvolume12
dc.subject.fieldofresearchGenetics
dc.subject.fieldofresearchMedical Biochemistry and Metabolomics
dc.subject.fieldofresearchOncology and Carcinogenesis
dc.subject.fieldofresearchcode0604
dc.subject.fieldofresearchcode1101
dc.subject.fieldofresearchcode1112
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsGenetics & Heredity
dc.subject.keywordsDementia with Lewy bodies
dc.subject.keywordsRisk prediction model
dc.titleA comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationShigemizu, D; Akiyama, S; Asanomi, Y; Boroevich, KA; Sharma, A; Tsunoda, T; Sakurai, T; Ozaki, K; Ochiya, T; Niida, S, A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data, BMC Medical Genomics, 2019, 12 (1)
dcterms.dateAccepted2019-10-18
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-01-31T00:44:59Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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