Xie Jize, Zheng Xubin, Yan Jianlong, Li Qizhi, Jin Nana, Wang Shuojia, Zhao Pengfei, Li Shuai, Ding Wanfu, Cheng Lixin, Geng Qingshan
Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen People's Hospital (First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University), Shenzhen 518020, China.
John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China.
iScience. 2024 May 7;27(6):109908. doi: 10.1016/j.isci.2024.109908. eCollection 2024 Jun 21.
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.
准确检测病原体，尤其是区分革兰氏阳性菌和革兰氏阴性菌，可改善疾病治疗。宿主基因表达能够捕捉免疫系统对各种病原体引起的感染的反应。在此，我们提出了一种深度神经网络模型bvnGPS2，该模型基于大规模综合宿主转录组数据集整合了注意力机制，以精确识别革兰氏阳性菌和革兰氏阴性菌感染以及病毒感染。我们使用之前设计的组学数据整合方法iPAGE，对来自10个国家40个队列的4949份血样进行分析，以选择判别基因对并训练bvnGPS2。该模型的性能在包含374个样本的六个独立队列上进行了评估。总体而言，我们的深度神经网络模型显示出准确识别特定感染的强大能力，为感染治疗中的精准医学策略以及潜在地识别其他疾病的亚型铺平了道路。
