Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression

• DOI: 10.1016/j.isci.2024.109908
• Publication Date: 2024-05-07T01:57:48Z
• Authors: Jize Xie; Xubin Zheng; Jianlong Yan; Qizhi Li; Nana Jin; Shuojia Wang; Pengfei Zhao; Shuai Li; Wanfu Ding; Lixin Cheng; Qingshan Geng
• Keywords: 0301 basic medicine; 03 medical and health sciences; Biocomputational method; Medical informatics; Science; Q; Clinical microbiology; Pathophysiology; Neural networks; Article

Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system's response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.