Modelling T-cell Activation Using Gene Expression Profiling and State Space Models

Research output: Contribution to journalArticle

Standard

Modelling T-cell Activation Using Gene Expression Profiling and State Space Models. / Rangel, C; Angus, J; Ghahramani, Z; Lioumi, M; Sotheran, E; Gaiba, A; Wild, DL; Falciani, Francesco.

In: Bioinformatics, Vol. 20, No. 9, 12.02.2004, p. 1361-1372.

Research output: Contribution to journalArticle

Harvard

Rangel, C, Angus, J, Ghahramani, Z, Lioumi, M, Sotheran, E, Gaiba, A, Wild, DL & Falciani, F 2004, 'Modelling T-cell Activation Using Gene Expression Profiling and State Space Models', Bioinformatics, vol. 20, no. 9, pp. 1361-1372. https://doi.org/10.1093/bioinformatics/bth093

APA

Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Sotheran, E., Gaiba, A., Wild, DL., & Falciani, F. (2004). Modelling T-cell Activation Using Gene Expression Profiling and State Space Models. Bioinformatics, 20(9), 1361-1372. https://doi.org/10.1093/bioinformatics/bth093

Vancouver

Rangel C, Angus J, Ghahramani Z, Lioumi M, Sotheran E, Gaiba A et al. Modelling T-cell Activation Using Gene Expression Profiling and State Space Models. Bioinformatics. 2004 Feb 12;20(9):1361-1372. https://doi.org/10.1093/bioinformatics/bth093

Author

Rangel, C ; Angus, J ; Ghahramani, Z ; Lioumi, M ; Sotheran, E ; Gaiba, A ; Wild, DL ; Falciani, Francesco. / Modelling T-cell Activation Using Gene Expression Profiling and State Space Models. In: Bioinformatics. 2004 ; Vol. 20, No. 9. pp. 1361-1372.

Bibtex

@article{b5f81a7eec464758882c18decf66f940,
title = "Modelling T-cell Activation Using Gene Expression Profiling and State Space Models",
abstract = "Motivation: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Results: Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses.",
author = "C Rangel and J Angus and Z Ghahramani and M Lioumi and E Sotheran and A Gaiba and DL Wild and Francesco Falciani",
year = "2004",
month = feb,
day = "12",
doi = "10.1093/bioinformatics/bth093",
language = "English",
volume = "20",
pages = "1361--1372",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "9",

}

RIS

TY - JOUR

T1 - Modelling T-cell Activation Using Gene Expression Profiling and State Space Models

AU - Rangel, C

AU - Angus, J

AU - Ghahramani, Z

AU - Lioumi, M

AU - Sotheran, E

AU - Gaiba, A

AU - Wild, DL

AU - Falciani, Francesco

PY - 2004/2/12

Y1 - 2004/2/12

N2 - Motivation: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Results: Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses.

AB - Motivation: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Results: Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses.

UR - http://www.scopus.com/inward/record.url?scp=3142744689&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bth093

DO - 10.1093/bioinformatics/bth093

M3 - Article

C2 - 14962938

VL - 20

SP - 1361

EP - 1372

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 9

ER -