ArticleEuropean journal of medical research2024
Epigenetic characterization of sarcopenia-associated genes based on machine learning and network screening.
Article in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Article
- Causal Implication of CD52-Driven Immune Dysregulation in Sarcopenic Obesity: Integrating Mendelian Randomization and Multiomics Profiling.Clinical interventions in aging · 2026Article
- Integrated bioinformatics and mendelian randomization reveal a six-gene diagnostic signature and key role of CYP26B1 in sarcopenia.Frontiers in molecular biosciences · 2026Article
- Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy.International journal of molecular sciences · 2025Review
- Diverse Roles of Tubulin Polymerization Promoting Protein 3 (TPPP3) in Human Health and Disease.Cells · 2025Review
- Comprehensive analysis of disulfidoptosis-related genes reveals molecular heterogeneity and key regulators in retinoblastoma progression.Translational pediatrics · 2025Article
- Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI.International journal of molecular sciences · 2025Review
- Proteomics and lipidomics of human umbilical cord mesenchymal stem cells exposed to ionizing radiation.European journal of medical research · 2025Article
- Systems biology and experimental validation indicate DDIT4, FOXO1, and STAT3 as shared key genes linking osteoporosis and sarcopenia.Frontiers in genetics · 2025Article
- Anesthesia decision analysis using a cloud-based big data platform.European journal of medical research · 2024Review
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.
Funding
Abstract
To screen characteristic genes related to sarcopenia by bioinformatics and machine learning, and to verify the accuracy of characteristic genes in the diagnosis of sarcopenia. Download myopia-related data sets from geo public database, find the differential genes through R language limma package after merging, STRING database to build protein interaction network, and do Go analysis and GSEA analysis to understand the functions and molecular signal pathways that may be affected by the differential genes. Further screen the characteristic genes through LASSO and SVM-RFE machine algorithms, make the ROC curve of the characteristic genes, and obtain the AUC value. 10 differential genes were obtained from the data set, including 7 upregulated genes and 3 downregulated genes. Eight characteristic genes were screened by a machine learning algorithm, and the AUC value of characteristic genes exceeded 0.7. In patients with sarcopenia, the expression of TPPP3, C1QA, LGR5, MYH8, and CDKN1A genes are upregulated, and the expression of SLC38A1, SERPINA5, and HOXB2 genes are downregulated. The above genes have high accuracy in the diagnosis of sarcopenia. The research results provide new ideas for the diagnosis and mechanism research of sarcopenia.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.