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Predicting Viral Infection From High-Dimensional Biomarker Trajectories.

Publication ,  Journal Article
Chen, M; Zaas, A; Woods, C; Ginsburg, GS; Lucas, J; Dunson, D; Carin, L
Published in: J Am Stat Assoc
January 1, 2011

There is often interest in predicting an individual's latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection. Using a nonparametric cure rate model for the latent initiation times, we allow selection of the genes in the viral response pathway, variability among individuals in infection times, and a subset of individuals who are not infected. As we demonstrate using held-out data, this statistical framework allows accurate predictions of infected individuals in advance of the development of clinical symptoms, without labeled data and even when the number of biomarkers vastly exceeds the number of individuals under study. Biological interpretation of several of the inferred pathways (factors) is provided.

Duke Scholars

Published In

J Am Stat Assoc

DOI

ISSN

0162-1459

Publication Date

January 1, 2011

Volume

106

Issue

496

Start / End Page

1259 / 1279

Location

United States

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

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ICMJE
MLA
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Chen, M., Zaas, A., Woods, C., Ginsburg, G. S., Lucas, J., Dunson, D., & Carin, L. (2011). Predicting Viral Infection From High-Dimensional Biomarker Trajectories. J Am Stat Assoc, 106(496), 1259–1279. https://doi.org/10.1198/jasa.2011.ap10611
Chen, Minhua, Aimee Zaas, Christopher Woods, Geoffrey S. Ginsburg, Joseph Lucas, David Dunson, and Lawrence Carin. “Predicting Viral Infection From High-Dimensional Biomarker Trajectories.J Am Stat Assoc 106, no. 496 (January 1, 2011): 1259–79. https://doi.org/10.1198/jasa.2011.ap10611.
Chen M, Zaas A, Woods C, Ginsburg GS, Lucas J, Dunson D, et al. Predicting Viral Infection From High-Dimensional Biomarker Trajectories. J Am Stat Assoc. 2011 Jan 1;106(496):1259–79.
Chen, Minhua, et al. “Predicting Viral Infection From High-Dimensional Biomarker Trajectories.J Am Stat Assoc, vol. 106, no. 496, Jan. 2011, pp. 1259–79. Pubmed, doi:10.1198/jasa.2011.ap10611.
Chen M, Zaas A, Woods C, Ginsburg GS, Lucas J, Dunson D, Carin L. Predicting Viral Infection From High-Dimensional Biomarker Trajectories. J Am Stat Assoc. 2011 Jan 1;106(496):1259–1279.
Journal cover image

Published In

J Am Stat Assoc

DOI

ISSN

0162-1459

Publication Date

January 1, 2011

Volume

106

Issue

496

Start / End Page

1259 / 1279

Location

United States

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics