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NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

Bellot P, Olsen C, Salembier P, Oliveras-Vergés A, Meyer PE - BMC Bioinformatics (2015)

Bottom Line: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data.As a result, it is possible to identify the techniques that have broad overall performances.

View Article: PubMed Central - PubMed

Affiliation: Universitat Politecnica de Catalunya BarcelonaTECH, Department of Signal Theory and Communications, UPC-Campus Nord, C/ Jordi Girona, 1-3, Barcelona, 08034, Spain. pau.bellot@upc.edu.

ABSTRACT

Background: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Results: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.

Conclusions: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

No MeSH data available.


Workflow of the network evaluation process
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Fig1: Workflow of the network evaluation process

Mentions: In order to provide a sound and fair comparison of the different methods, the use of various simulators is essential. A large set of gene expressions generated by various simulators is collected in what we call “Datasource” (see Fig. 1).Fig. 1


NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

Bellot P, Olsen C, Salembier P, Oliveras-Vergés A, Meyer PE - BMC Bioinformatics (2015)

Workflow of the network evaluation process
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4587916&req=5

Fig1: Workflow of the network evaluation process
Mentions: In order to provide a sound and fair comparison of the different methods, the use of various simulators is essential. A large set of gene expressions generated by various simulators is collected in what we call “Datasource” (see Fig. 1).Fig. 1

Bottom Line: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data.As a result, it is possible to identify the techniques that have broad overall performances.

View Article: PubMed Central - PubMed

Affiliation: Universitat Politecnica de Catalunya BarcelonaTECH, Department of Signal Theory and Communications, UPC-Campus Nord, C/ Jordi Girona, 1-3, Barcelona, 08034, Spain. pau.bellot@upc.edu.

ABSTRACT

Background: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Results: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.

Conclusions: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

No MeSH data available.