ArticleAging2022
Identification of combined biomarkers for predicting the risk of osteoporosis using machine learning.
Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- A case-control study coupling with meta-analysis elaborates decisive association betweenAging · 2023Pooled it
- Gene screening and immune phenotype shifts of osteoporosis.Scientific reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Relationship between albumin-corrected anion gap and lumbar spine bone mineral density: a cross-sectional study.Frontiers in aging · 2025Article
- Decoding the Tumor Microenvironment of Myoepithelial Cells in Triple-Negative Breast Cancer Through Single-Cell and Transcriptomic Sequencing and Establishing a Prognostic Model Based on Key Myoepithelial Cell Genes.International journal of genomics · 2025Article
- PFDN6 contributes to colorectal cancer progression via transcriptional regulation.eGastroenterology · 2024Article
- Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.Frontiers in immunology · 2024Article
- A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study.JMIR aging · 2023Article
- Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China.Heliyon · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis is a severe chronic skeletal disorder that affects older individuals, especially postmenopausal women. However, molecular biomarkers for predicting the risk of osteoporosis are not well characterized. The aim of this study was to identify combined biomarkers for predicting the risk of osteoporosis using machine learning methods. We merged three publicly available gene expression datasets (GSE56815, GSE13850, and GSE2208) to obtain expression data for 6354 unique genes in postmenopausal women (45 with high bone mineral density and 45 with low bone mineral density). All machine learning methods were implemented in R, with the GEOquery and limma packages, for dataset download and differentially expressed gene identification, and a nomogram for predicting the risk of osteoporosis was constructed. We detected 378 significant differentially expressed genes using the limma package, representing 15 major biological pathways. The performance of the predictive models based on combined biomarkers (two or three genes) was superior to that of models based on a single gene. The best predictive gene set among two-gene sets included
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.