ArticlePathogens & immunity2024
The TB27 Transcriptomic Model for Predicting
Article in Pathogens & immunity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Host response biomarkers of tuberculosis recurrence and treatment failure.Communications medicine · 2026Article
- PD-L1-centric whole blood-based immune signature profiles of tuberculosis patients during therapy.Frontiers in immunology · 2026Article
- Host-response biomarkers for precision tuberculosis: from immune transcriptomic signatures to clinical risk stratification.Frontiers in cell and developmental biology · 2026Review
- Analysis of gene expression in patients prior to TB treatment to identify those associated with pyrazinamide-hepatotoxicity.IJTLD open · 2025Article
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Authors and funding
28 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy. Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy. Results: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of Conclusion: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to
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