ArticleOpen medicine (Warsaw, Poland)2022
Identification of osteoporosis based on gene biomarkers using support vector machine.
Article in Open medicine (Warsaw, Poland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Systems biology and experimental validation indicate DDIT4, FOXO1, and STAT3 as shared key genes linking osteoporosis and sarcopenia.Frontiers in genetics · 2025Article
- Molecularly Imprinted Polymers: Shaping the Future of Early-Stage Bone Loss Detection-A Review.ACS omega · 2024Review
- A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study.JMIR aging · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis is a major health concern worldwide. The present study aimed to identify effective biomarkers for osteoporosis detection. In osteoporosis, 559 differentially expressed genes (DEGs) were enriched in PI3K-Akt signaling pathway and Foxo signaling pathway. Weighted gene co-expression network analysis showed that green, pink, and tan modules were clinically significant modules, and that six genes (VEGFA, DDX5, SOD2, HNRNPD, EIF5B, and HSP90B1) were identified as "real" hub genes in the protein-protein interaction network, co-expression network, and 559 DEGs. The sensitivity and specificity of the support vector machine (SVM) for identifying patients with osteoporosis was 100%, with an area under curve of 1 in both training and validation datasets. Our results indicated that the current system using the SVM method could identify patients with osteoporosis.
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