Author:
Aminian M.,Ghosh T.,Peterson A.,Rasmussen A. L.,Stiverson S.,Sharma K.,Kirby M.
Abstract
AbstractThis paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infected with HRV, RSV, H1N1, and H3N2. We address the problem of identifying discriminatory biomarkers between controls and eventual shedders in the first 32 h post-infection. Our exploratory analysis shows that the most discriminatory biomarkers exhibit a strong dependence on time over the course of the human response to infection. We visualize the feature sets to provide evidence of the rapid evolution of the gene expression profiles. To quantify this observation, we partition the data in the first 32 h into four equal time windows of 8 h each and identify all discriminatory biomarkers using sparsity-promoting classifiers and Iterated Feature Removal. We then perform a comparative machine learning classification analysis using linear support vector machines, artificial neural networks and Centroid-Encoder. We present a range of experiments on different groupings of the diseases to demonstrate the robustness of the resulting models.
Funder
Defense Advanced Research Projects Agency
Publisher
Springer Science and Business Media LLC
Reference36 articles.
1. Ip, D. K. et al. The dynamic relationship between clinical symptomatology and viral shedding in naturally acquired seasonal and pandemic influenza virus infections. Clin. Infect. Dis. 62(4), 431–437 (2016).
2. Leung, N. H., Xu, C., Ip, D. K. & Cowling, B. J. The fraction of influenza virus infections that are asymptomatic: A systematic review and meta-analysis. Epidemiology (Camb., Mass.) 26(6), 862 (2015).
3. He, X. et al. Temporal dynamics in viral shedding and transmissibility of covid-19. Nat. Med. 2, 1–4 (2020).
4. Cooper, L. et al. Pareto rules for malaria super-spreaders and super-spreading. Nat. Commun. 10(1), 1–9 (2019).
5. Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438(7066), 355–359 (2005).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献