Dataset from Zenodo · fırat, yelda
Predicting Long COVID Persistence Using Mitochondrial Gene Expression Profiles: An XGBoost-Based Machine Learning Approach with External Validation
- License
- CC BY 4.0commercial use allowedLicense text
- This license allows commercial use. Usually you must credit the author; read the license for the exact terms.
- Formats
- GZ, ZIP, MD
- Size
- 54 MB
- Updated
- Jul 16, 2026
Description
This repository contains the complete analysis pipeline, preprocessed datasets, and trained machine learning models supporting the manuscript: "Predicting Long COVID Persistence Using Mitochondrial Gene Expression Profiles: An XGBoost-Based Machine Learning Approach with External Validation" Study Overview: We developed an XGBoost-based machine learning classifier using 149 mitochondrial gene expression profiles (MitoCarta3.0) from peripheral blood transcriptomes of 343 individuals across 6 independent GEO cohorts (GSE267625, GSE169687, GSE152418, GSE222253, GSE265753, GSE235938). The model distinguishes Long COVID persistence from recovery with high accuracy (nested CV AUC = 0.999 ± 0.001; external validation AUC = 0.984, 95% CI: 0.959–1.000). Repository Contents: longcovid ML FULL RESULTS.zip — Complete analysis package including Python scripts, trained XGBoost model, cross-validation results, feature importance rankings, and publication-quality figures (TIFF, 300 DPI). X full transcriptome log2cpm.csv.gz — Full transcriptome expression matrix (18,335 genes × 343 samples, log2-CPM normalized, ComBat batch-corrected). X mito genes log2cpm.csv.gz…
Description from Zenodo, shortened.
Files (4)
- GZX_full_transcriptome_log2cpm.csv.gz45 MB
- GZX_mito_genes_log2cpm.csv.gz448 kB
- ZIPlongcovid_ML_FULL_RESULTS.zip8.7 MB
- MDREADME.md6.8 kB
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