ArticleHeliyon2024
Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Identifying AIM2 Circulating Methylation Levels as a Novel Diagnostic Biomarker for Rheumatoid Arthritis Using Targeted DNA Methylation Sequencing.Endocrine, metabolic & immune disorders drug targets · 2026Article
- Integrating machine learning with SHAP to uncover multi-tissue molecular signatures in Osteoarthritis progression.PloS one · 2026Article
- Nuclear binding SET domain 1 alleviates cartilage ferroptosis in knee osteoarthritis by upregulating the krüppel-like factor 9/autophagy-related 14 pathway via H3K36me2 modification.Journal of cell communication and signaling · 2025Article
- Knee Osteoarthritis Diagnosis: Future and Perspectives.Biomedicines · 2025Review
- Tuina therapy alleviates knee osteoarthritis by modulating PI3K/AKT/mTOR-mediated autophagy: an integrated machine learning andFrontiers in immunology · 2025Article
- Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis.Journal of orthopaedic surgery and research · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples. Methods: We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers. Results: In total, 251 DEGs were screened, where Conclusion: We identified a combined model for early diagnosis of OA that includes
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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.