Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics.

ABSTRACT: The prompt diagnosis of pulmonary infections with unknown etiology in patients in severe condition remains a challenge due to the lack of rapid and effective diagnostic methods. While metatranscriptomic sequencing offers a powerful approach, its clinical utility is often limited by issues of timeliness. In this study, we conducted metatranscriptomic sequencing on bronchoalveolar lavage fluid (BALF) collected from critically ill, severely ill, and ICU patients. Based on microbial detection results, patients were classified into four types: negative, bacterial infection, viral infection, and fungal infection. To identify host gene expression signatures associated with infection, we screened characteristic genes from human metatranscriptomic data by comparing 70% of patients with confirmed in

SUBMITTER: Zhang T 

PROVIDER: S-EPMC12368225 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature