Sparse Bayesian promoter based gene classification

K. K. Lee; G. C. Cawley; M. W. Bevan

Sparse Bayesian promoter based gene classification

K. K. Lee, G. C. Cawley, M. W. Bevan

Research output: Contribution to conference › Paper

Abstract

A method to distinguish between co-regulated genes that are up- or down-regulated under a given treatment, based on the composition of the upstrem promoter region, would be a valuable tool in deciphering gene regulatory networks. Ideally, the classification should be based on a small number of regulatory motifs, whos presence in the promoter region of a gene induce a significant effect on its transcriptional regulation. In this paper, we investigate the use of Relevance Vector Machines for this task, and present initial results of an analysis of glucose response in the model plant Arabidopsis thaliana, that has revealed novel biological information.

Original language	English
Pages	527-532
Number of pages	6
Publication status	Published - Apr 2005
Event	European Symposium on Artificial Neural Networks - Bruges, Belgium Duration: 27 Apr 2005 → 29 Apr 2005

Conference

Conference	European Symposium on Artificial Neural Networks
Abbreviated title	ESANN-2005
Country/Territory	Belgium
City	Bruges
Period	27/04/05 → 29/04/05

Cite this

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title = "Sparse Bayesian promoter based gene classification",

abstract = "A method to distinguish between co-regulated genes that are up- or down-regulated under a given treatment, based on the composition of the upstrem promoter region, would be a valuable tool in deciphering gene regulatory networks. Ideally, the classification should be based on a small number of regulatory motifs, whos presence in the promoter region of a gene induce a significant effect on its transcriptional regulation. In this paper, we investigate the use of Relevance Vector Machines for this task, and present initial results of an analysis of glucose response in the model plant Arabidopsis thaliana, that has revealed novel biological information.",

author = "Lee, {K. K.} and Cawley, {G. C.} and Bevan, {M. W.}",

year = "2005",

month = apr,

language = "English",

pages = "527--532",

note = "European Symposium on Artificial Neural Networks, ESANN-2005 ; Conference date: 27-04-2005 Through 29-04-2005",

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TY - CONF

T1 - Sparse Bayesian promoter based gene classification

AU - Lee, K. K.

AU - Cawley, G. C.

AU - Bevan, M. W.

PY - 2005/4

Y1 - 2005/4

N2 - A method to distinguish between co-regulated genes that are up- or down-regulated under a given treatment, based on the composition of the upstrem promoter region, would be a valuable tool in deciphering gene regulatory networks. Ideally, the classification should be based on a small number of regulatory motifs, whos presence in the promoter region of a gene induce a significant effect on its transcriptional regulation. In this paper, we investigate the use of Relevance Vector Machines for this task, and present initial results of an analysis of glucose response in the model plant Arabidopsis thaliana, that has revealed novel biological information.

AB - A method to distinguish between co-regulated genes that are up- or down-regulated under a given treatment, based on the composition of the upstrem promoter region, would be a valuable tool in deciphering gene regulatory networks. Ideally, the classification should be based on a small number of regulatory motifs, whos presence in the promoter region of a gene induce a significant effect on its transcriptional regulation. In this paper, we investigate the use of Relevance Vector Machines for this task, and present initial results of an analysis of glucose response in the model plant Arabidopsis thaliana, that has revealed novel biological information.

M3 - Paper

SP - 527

EP - 532

T2 - European Symposium on Artificial Neural Networks

Y2 - 27 April 2005 through 29 April 2005

ER -