PDF(Pubmed)
Abstract：
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.
摘要：
准确检测病原体，特别区分革兰氏阳性和革兰氏阴性细菌，可以改善疾病治疗。宿主基因表达可以捕获免疫系统对各种病原体引起的感染的反应。这里,我们提出了一个深度神经网络模型，bvnGPS2，它结合了基于大规模整合宿主转录组数据集的注意力机制，以精确识别革兰氏阳性和革兰氏阴性细菌感染以及病毒感染。我们使用我们先前设计的组学数据整合方法，对来自10个国家的40个队列的4,949个血液样本进行了分析。iPAGE,选择判别式基因对并训练bvnGPS2。在包含374个样品的6个独立队列上评估模型的性能。总的来说,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，为感染治疗中的精确医学策略铺平了道路，也可能为识别其他疾病的亚型铺平了道路。
