Document Type
Article
Publication Date
2-24-2021
Publication Title
Scientific Reports
Volume
11
Issue
1
First page number:
1
Last page number:
10
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.
Disciplines
Computer Sciences | Genomics
File Format
File Size
1954 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Wu, Q.,
Nasoz, F.,
Jung, J.,
Bhattarai, B.,
Han, M.,
Greenes, R.,
Saag, K.
(2021).
Machine Learning Approaches for the Prediction of Bone Mineral Density by Using Genomic and Phenotypic Data of 5130 Older Men.
Scientific Reports, 11(1),
1-10.
http://dx.doi.org/10.1038/s41598-021-83828-3