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Bagging statistical network inference from large-scale gene expression data.

de Matos Simoes R, Emmert-Streib F - PLoS ONE (2012)

Bottom Line: Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions.Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences.An implementation of BC3NET is freely available as an R package from the CRAN repository.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom.

ABSTRACT
Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository.

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Comparative analysis of BC3NET, GENIE3 and C3NET for Erdös-Rényi networks with edge density. The x-axis shows the sample size .
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pone-0033624-g004: Comparative analysis of BC3NET, GENIE3 and C3NET for Erdös-Rényi networks with edge density. The x-axis shows the sample size .

Mentions: In order to gain insight into the quality of BC3NET we study it comparatively by contrasting its performance with GENIE3 and C3NET. In Fig. 4 we show results for three different Erdös-Rényi networks each with genes, of which genes are unconnected. The edge density of these networks is . We use these edge densities because regulatory networks are known to be sparsely connected [50]. The F-score distributions for all studied conditions are larger for BC3NET. We repeated the above simulations for subnetworks from the transcriptional regulatory network of E. coli and obtained qualitatively similar results. This demonstrates the robustness of the results with respect to different network types and network parameters.


Bagging statistical network inference from large-scale gene expression data.

de Matos Simoes R, Emmert-Streib F - PLoS ONE (2012)

Comparative analysis of BC3NET, GENIE3 and C3NET for Erdös-Rényi networks with edge density. The x-axis shows the sample size .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3316596&req=5

pone-0033624-g004: Comparative analysis of BC3NET, GENIE3 and C3NET for Erdös-Rényi networks with edge density. The x-axis shows the sample size .
Mentions: In order to gain insight into the quality of BC3NET we study it comparatively by contrasting its performance with GENIE3 and C3NET. In Fig. 4 we show results for three different Erdös-Rényi networks each with genes, of which genes are unconnected. The edge density of these networks is . We use these edge densities because regulatory networks are known to be sparsely connected [50]. The F-score distributions for all studied conditions are larger for BC3NET. We repeated the above simulations for subnetworks from the transcriptional regulatory network of E. coli and obtained qualitatively similar results. This demonstrates the robustness of the results with respect to different network types and network parameters.

Bottom Line: Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions.Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences.An implementation of BC3NET is freely available as an R package from the CRAN repository.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom.

ABSTRACT
Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository.

Show MeSH