PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes

Changchun Wu; Xuejun Xie; Zixin Huang; Yuwei Zhou; Yushu Gou; Mengze Du; Hao Lin; J. L. HUANG

doi:10.1186/s12915-026-02585-9

March 27, 2026BMC BiologyOpen Access

PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes

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Key Points

The aim is to create a non-invasive diagnostic model for pulmonary tuberculosis using blood transcriptomic data.
Developed a machine learning model using large-scale blood transcriptomes.
Identified diagnostic gene-pair expression patterns.
Validated the model's applicability for early diagnosis.
The model demonstrates effectiveness in diagnosing pulmonary tuberculosis.
Uncovered five gene-pair expression patterns linked to the disease.
Potentially reduces the burden of pulmonary tuberculosis through timely intervention.

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Implication

Develops a machine learning model to diagnose pulmonary tuberculosis through blood transcriptomes, indicating its potential for early interventions.

Abstract

This study provides a broadly applicable tool for early PTB diagnosis, facilitating timely intervention and potentially reducing global PTB burden. Moreover, PTBD uncovers novel transcriptomic biomarkers, represented by five diagnostic gene-pair expression patterns that embody the molecular hallmarks of PTB.

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Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/69c8c3a8de0f0f753b39e99a https://doi.org/10.1186/s12915-026-02585-9

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PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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