Identifying submodules of cellular regulatory networks

Guido SanguinettiMagnus RattrayNeil D. Lawrence
, 2006.

Abstract

Recent high throughput techniques in molecular biology have brought about the possibility of directly identifying the architecture of regulatory networks on a genome-wide scale. However, the computational task of estimating fine-grained models on a genome-wide scale is daunting. Therefore, it is of great importance to be able to reliably identify submodules of the network that can be effectively modelled as independent subunits. In this paper we present a procedure to obtain submodules of a cellular network by using information from gene-expression measurements. We integrate network architecture data with genome-wide gene expression measurements in order to determine which regulatory relations are actually confirmed by the expression data. We then use this information to obtain non-trivial submodules of the regulatory network using two distinct algorithms, a naive exhaustive algorithm and a spectral algorithm based on the eigendecomposition of an affinity matrix. We test our method on two yeast biological data sets, using regulatory information obtained from chromatin immunoprecipitation.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-sanguinetti-trento06, title = {Identifying submodules of cellular regulatory networks}, author = {Guido Sanguinetti and Magnus Rattray and Neil D. Lawrence}, year = {}, editor = {}, url = {http://inverseprobability.com/publications/sanguinetti-trento06.html}, abstract = {Recent high throughput techniques in molecular biology have brought about the possibility of directly identifying the architecture of regulatory networks on a genome-wide scale. However, the computational task of estimating fine-grained models on a genome-wide scale is daunting. Therefore, it is of great importance to be able to reliably identify submodules of the network that can be effectively modelled as independent subunits. In this paper we present a procedure to obtain submodules of a cellular network by using information from gene-expression measurements. We integrate network architecture data with genome-wide gene expression measurements in order to determine which regulatory relations are actually confirmed by the expression data. We then use this information to obtain non-trivial submodules of the regulatory network using two distinct algorithms, a naive exhaustive algorithm and a spectral algorithm based on the eigendecomposition of an affinity matrix. We test our method on two yeast biological data sets, using regulatory information obtained from chromatin immunoprecipitation.} }
Endnote
%0 Conference Paper %T Identifying submodules of cellular regulatory networks %A Guido Sanguinetti %A Magnus Rattray %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-sanguinetti-trento06 %I PMLR %J Proceedings of Machine Learning Research %P -- %R 10.1007/11885191_11 %U http://inverseprobability.com %V %W PMLR %X Recent high throughput techniques in molecular biology have brought about the possibility of directly identifying the architecture of regulatory networks on a genome-wide scale. However, the computational task of estimating fine-grained models on a genome-wide scale is daunting. Therefore, it is of great importance to be able to reliably identify submodules of the network that can be effectively modelled as independent subunits. In this paper we present a procedure to obtain submodules of a cellular network by using information from gene-expression measurements. We integrate network architecture data with genome-wide gene expression measurements in order to determine which regulatory relations are actually confirmed by the expression data. We then use this information to obtain non-trivial submodules of the regulatory network using two distinct algorithms, a naive exhaustive algorithm and a spectral algorithm based on the eigendecomposition of an affinity matrix. We test our method on two yeast biological data sets, using regulatory information obtained from chromatin immunoprecipitation.
RIS
TY - CPAPER TI - Identifying submodules of cellular regulatory networks AU - Guido Sanguinetti AU - Magnus Rattray AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-sanguinetti-trento06 PB - PMLR SP - DP - PMLR EP - DO - 10.1007/11885191_11 L1 - UR - http://inverseprobability.com/publications/sanguinetti-trento06.html AB - Recent high throughput techniques in molecular biology have brought about the possibility of directly identifying the architecture of regulatory networks on a genome-wide scale. However, the computational task of estimating fine-grained models on a genome-wide scale is daunting. Therefore, it is of great importance to be able to reliably identify submodules of the network that can be effectively modelled as independent subunits. In this paper we present a procedure to obtain submodules of a cellular network by using information from gene-expression measurements. We integrate network architecture data with genome-wide gene expression measurements in order to determine which regulatory relations are actually confirmed by the expression data. We then use this information to obtain non-trivial submodules of the regulatory network using two distinct algorithms, a naive exhaustive algorithm and a spectral algorithm based on the eigendecomposition of an affinity matrix. We test our method on two yeast biological data sets, using regulatory information obtained from chromatin immunoprecipitation. ER -
APA
Sanguinetti, G., Rattray, M. & Lawrence, N.D.. (). Identifying submodules of cellular regulatory networks. , in PMLR :-

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