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

Abstract 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 analyze...

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Autores principales:	Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han, Robert A. Greenes, Kenneth G. Saag
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d62fca4849fd4679a89037fdc1c465b7
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spelling	oai:doaj.org-article:d62fca4849fd4679a89037fdc1c465b72021-12-02T13:35:03ZMachine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men10.1038/s41598-021-83828-32045-2322https://doaj.org/article/d62fca4849fd4679a89037fdc1c465b72021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83828-3https://doaj.org/toc/2045-2322Abstract 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 imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold cross-validation was used for hyper-parameters optimization. Mean square error and mean absolute error were used to assess model performance. When using GRS and phenotypic covariates as the predictors, all ML models’ performance and linear regression in BMD prediction were similar. However, when replacing GRS with the 1103 individual SNPs in the model, ML models performed significantly better than linear regression (with lasso regularization), and the gradient boosting model performed the best. Our study suggested that ML models, especially gradient boosting, can improve BMD prediction in genomic data.Qing WuFatma NasozJongyun JungBibek BhattaraiMira V. HanRobert A. GreenesKenneth G. SaagNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qing Wu Fatma Nasoz Jongyun Jung Bibek Bhattarai Mira V. Han Robert A. Greenes Kenneth G. Saag Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men
description	Abstract 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 imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold cross-validation was used for hyper-parameters optimization. Mean square error and mean absolute error were used to assess model performance. When using GRS and phenotypic covariates as the predictors, all ML models’ performance and linear regression in BMD prediction were similar. However, when replacing GRS with the 1103 individual SNPs in the model, ML models performed significantly better than linear regression (with lasso regularization), and the gradient boosting model performed the best. Our study suggested that ML models, especially gradient boosting, can improve BMD prediction in genomic data.
format	article
author	Qing Wu Fatma Nasoz Jongyun Jung Bibek Bhattarai Mira V. Han Robert A. Greenes Kenneth G. Saag
author_facet	Qing Wu Fatma Nasoz Jongyun Jung Bibek Bhattarai Mira V. Han Robert A. Greenes Kenneth G. Saag
author_sort	Qing Wu
title	Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men
title_short	Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men
title_full	Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men
title_fullStr	Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men
title_full_unstemmed	Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men
title_sort	machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d62fca4849fd4679a89037fdc1c465b7
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Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men

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