PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes
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.
Authors
- Xuejun Xie (ORCID: https://orcid.org/0000-0002-1161-3836)
- Yushu Gou (ORCID: https://orcid.org/0009-0008-4613-3024)
- Yuwei Zhou (ORCID: https://orcid.org/0000-0001-9582-7331)
- Hao Lin (ORCID: https://orcid.org/0000-0001-7981-7707)
- Zixin Huang (ORCID: https://orcid.org/0000-0002-6529-8691)
- J. L. HUANG
- Changchun Wu (ORCID: https://orcid.org/0009-0003-9864-2756)
- Mengze Du
Institutions
- University of Electronic Science and Technology of China (CN)
- Chengdu University of Technology (CN)
- Neusoft (China) (CN)
- Chengdu Neusoft University
Publication Details
- Journal
- BMC Biology
- Published
- 2026-03-27
- DOI
- https://doi.org/10.1186/s12915-026-02585-9
- Primary Topic
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00