A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without...

Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methylation-driven genes (MDGs) of host blood could be used to diagnose patients co-infected with HIV/TB. In this study, we identified three MDGs among patients with HIV monoinfection and those with HIV/TB co-infection using the R package MethylMix. We then developed a deep learning model by screening these three MDGs, which distinguished HIV/TB co-infection from HIV monoinfection with a sensitivity of 95.2% and a specificity of 88.3%. On the two independent data sets, the sensitivity and specificity were 80%-92.8% and 72.7%-87.5%, respectively. Besides, our deep learning model accurately classified TB (sensitivity, 75.0%-100%; specificity, 91.3%-98.1%) and other respiratory disorders (sensitivity, 72.7%-75.0%; specificity, 70.9%-72.7%). This study will contribute to improve molecular diagnosis for HIV/TB co-infection.