Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
Department of Mathematics, California State Polytechnic University, Pomona, CA, USA.
Sci Rep. 2023 Apr 1;13(1):5340. doi: 10.1038/s41598-023-30306-7.
Given an infected host, estimating the time that has elapsed since initial exposure to the pathogen is an important problem in public health. In this paper we use longitudinal gene expression data from human challenge studies of viral respiratory illnesses for building predictive models to estimate the time elapsed since onset of respiratory infection. We apply sparsity driven machine learning to this time-stamped gene expression data to model the time of exposure by a pathogen and subsequent infection accompanied by the onset of the host immune response. These predictive models exploit the fact that the host gene expression profile evolves in time and its characteristic temporal signature can be effectively modeled using a small number of features. Predicting the time of exposure to infection to be in first 48 h after exposure produces BSR in the range of 80-90% on sequestered test data. A variety of machine learning experiments provide evidence that models developed on one virus can be used to predict exposure time for other viruses, e.g., H1N1, H3N2, and HRV. The interferon [Formula: see text] signaling pathway appears to play a central role in keeping time from onset of infection. Successful prediction of the time of exposure to a pathogen has potential ramifications for patient treatment and contact tracing.
对于已感染的宿主，估计其从最初接触病原体到现在的时间是公共卫生领域的一个重要问题。在本文中，我们使用人类病毒呼吸道感染挑战研究中的纵向基因表达数据来构建预测模型，以估计呼吸道感染开始以来的时间流逝。我们将稀疏驱动的机器学习应用于这种时间标记的基因表达数据，以通过病原体的暴露时间和随后的感染以及宿主免疫反应的开始来建立模型。这些预测模型利用了宿主基因表达谱随时间演变的事实，并且其特征时间签名可以使用少数特征有效地建模。在隔离的测试数据上，预测暴露于感染的时间在前 48 小时内，BSR 在 80-90%的范围内。各种机器学习实验提供的证据表明，在一种病毒上开发的模型可用于预测其他病毒（例如 H1N1、H3N2 和 HRV）的暴露时间。干扰素 [Formula: see text] 信号通路似乎在保持感染开始时的时间方面起着核心作用。成功预测接触病原体的时间可能会对患者治疗和接触者追踪产生影响。
