tetano
Editor, Senior Moderator
Sci Rep
. 2023 Apr 1;13(1):5340.
doi: 10.1038/s41598-023-30306-7.
Using machine learning to determine the time of exposure to infection by a respiratory pathogen
Kartikay Sharma[SUP] 1 [/SUP], Manuchehr Aminian[SUP] 2 [/SUP], Tomojit Ghosh[SUP] 1 [/SUP], Xiaoyu Liu[SUP] 3 [/SUP], Michael Kirby[SUP] 4 5 [/SUP]
Affiliations
Abstract
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.
. 2023 Apr 1;13(1):5340.
doi: 10.1038/s41598-023-30306-7.
Using machine learning to determine the time of exposure to infection by a respiratory pathogen
Kartikay Sharma[SUP] 1 [/SUP], Manuchehr Aminian[SUP] 2 [/SUP], Tomojit Ghosh[SUP] 1 [/SUP], Xiaoyu Liu[SUP] 3 [/SUP], Michael Kirby[SUP] 4 5 [/SUP]
Affiliations
- PMID: 37005391
- PMCID: PMC10067823
- DOI: 10.1038/s41598-023-30306-7
Abstract
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.