Detection of viruses via statistical gene expression analysis

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

doi:10.1109/TBME.2010.2059702

Detection of viruses via statistical gene expression analysis

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

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

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. © 2006 IEEE.

Original language	English (US)
Pages (from-to)	468-479
Number of pages	12
Journal	IEEE Transactions on Biomedical Engineering
Volume	58
Issue number	3 PART 1
DOIs	https://doi.org/10.1109/TBME.2010.2059702
State	Published - Mar 1 2011
Externally published	Yes

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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. {\textcopyright} 2006 IEEE.",

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AU - Chen, Minhua

AU - Carlson, David

AU - Zaas, Aimee

AU - Woods, Christopher W.

AU - Ginsburg, Geoffrey S.

AU - Hero, Alfred

AU - Lucas, Joseph

AU - Carin, Lawrence

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N2 - 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. © 2006 IEEE.

AB - 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. © 2006 IEEE.

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JO - IEEE Transactions on Biomedical Engineering

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Detection of viruses via statistical gene expression analysis

Abstract

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