ArticleScientific reports2021
Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- An explainable predictive machine learning model of osteopenia for perimenopausal women based on clinical data: a retrospective single-center study.Frontiers in endocrinology · 2026Article
- Predicting anthropometric body composition variables using 3D optical imaging and machine learning.Frontiers in bioinformatics · 2026Article
- Emerging applications of feature selection in osteoporosis research: from biomarker discovery to clinical decision support.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025Review
- Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters.Endocrinology and metabolism (Seoul, Korea) · 2025Article
- Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- [Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].Problemy endokrinologii · 2024Article
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Article
- Risk factors associated with skeletal-related events following discontinuation of denosumab treatment among patients with bone metastases from solid tumors: A real-world machine learning approach.Journal of bone oncology · 2022Article
- Machine Learning to Predict the Progression of Bone Mass Loss Associated with Personal Characteristics and a Metabolic Syndrome Scoring Index.Healthcare (Basel, Switzerland) · 2021Article
- Large Scale Identification of Osteosarcoma Pathogenic Genes by Multiple Extreme Learning Machine.Frontiers in cell and developmental biology · 2021Article
- Identification of New Genes and Loci Associated With Bone Mineral Density Based on Mendelian Randomization.Frontiers in genetics · 2021Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
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.
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