title: Detection of viruses via statistical gene expression analysis.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Duke University, Durham, NC 90291 USA.
authors.forename: Minhua
authors.initials: M
authors.lastname: Chen
authors.forename: David
authors.initials: D
authors.lastname: Carlson
authors.forename: Aimee
authors.initials: A
authors.lastname: Zaas
authors.forename: Christopher W
authors.initials: CW
authors.lastname: Woods
authors.forename: Geoffrey S
authors.initials: GS
authors.lastname: Ginsburg
authors.forename: Alfred
authors.initials: A
authors.lastname: Hero
authors.forename: Joseph
authors.initials: J
authors.lastname: Lucas
authors.forename: Lawrence
authors.initials: L
authors.lastname: Carin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D054884:Host-Pathogen Interactions; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D053122:Influenza A Virus, H3N2 Subtype; D012136:Respiratory Syncytial Viruses; D012229:Rhinovirus; D014777:Virus Diseases / Q000821:virology*