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Article

Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men.

Scientific reports - 24 Feb 2021

Wu Qing, Nasoz Fatma, Jung Jongyun, Bhattarai Bibek, Han Mira V, Greenes Robert A, Saag Kenneth G

Abstract excerpt

The study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype...

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Identifiers

PMID: 33627720
DOI: 10.1038/s41598-021-83828-3
PMCID: PMC7904941

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