Detection of Viruses Via Statistical Gene Expression Analysis

Author

Chen, Minhua ; Carlson, David ; Zaas, Aimee ; Woods, Christopher ; Ginsburg, Geoffrey S. ; Hero, Alfred, III ; Lucas, Joseph ; Carin, Lawrence

Author_Institution

Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA

Volume

58

Issue

3

fYear

2011

fDate

3/1/2011 12:00:00 AM

Firstpage

468

Lastpage

479

Abstract

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.

Keywords

Bayes methods; bioinformatics; diseases; genetics; microorganisms; statistical analysis; Bayesian ENet; Bayesian construction; H1N1 influenza; H3N2 influenza; Rhino virus; elastic net; respiratory syncytial virus; statistical gene expression analysis; symptom; variational Bayesian analysis; Bayesian methods; Biological system modeling; Data analysis; Gene expression; Genomics; Humans; Influenza; Linear regression; Permission; Viruses (medical); Bayesian Lasso; elastic net (ENet); grouping effect; multitask learning; variable selection; Algorithms; Artificial Intelligence; Bayes Theorem; Computational Biology; Gene Expression Profiling; Host-Pathogen Interactions; Humans; Influenza A Virus, H1N1 Subtype; Influenza A Virus, H3N2 Subtype; Respiratory Syncytial Viruses; Rhinovirus; Virus Diseases;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2010.2059702

Filename

5512621