Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data.

ABSTRACT: There are many subtypes of dementia, and identification of diagnostic biomarkers that are minimally-invasive, low-cost, and efficient is desired. Circulating microRNAs (miRNAs) have recently gained attention as easily accessible and non-invasive biomarkers. We conducted a comprehensive miRNA expression analysis of serum samples from 1348 Japanese dementia patients, composed of four subtypes-Alzheimer's disease (AD), vascular dementia, dementia with Lewy bodies (DLB), and normal pressure hydrocephalus-and 246 control subjects. We used this data to construct dementia subtype prediction models based on penalized regression models with the multiclass classification. We constructed a final prediction model using 46 miRNAs, which classified dementia patients from an independent validation set in

SUBMITTER: Asanomi Y 

PROVIDER: S-EPMC8536697 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature