Detection of viruses via statistical gene expression analysis.

Journal Article (Journal Article)

We develop a new bayesian construction of the elastic net (ENet), with variational bayesian analysis. This modeling framework is motivated by analysis of gene expression data for viruses, with a focus on H3N2 and H1N1 influenza, as well as Rhino virus and RSV (respiratory syncytial virus). Our objective is to understand the biological pathways responsible for the host response to such viruses, with the ultimate objective of developing a clinical test to distinguish subjects infected by such viruses from subjects with other symptom causes (e.g., bacteria). In addition to analyzing these new datasets, we provide a detailed analysis of the bayesian ENet and compare it to related models.

Full Text

Duke Authors

Cited Authors

  • Chen, M; Carlson, D; Zaas, A; Woods, CW; Ginsburg, GS; Hero, A; Lucas, J; Carin, L

Published Date

  • March 2011

Published In

Volume / Issue

  • 58 / 3

Start / End Page

  • 468 - 479

PubMed ID

  • 20643599

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

Digital Object Identifier (DOI)

  • 10.1109/TBME.2010.2059702


  • eng

Conference Location

  • United States