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GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods


Schaffter, Thomas; Marbach, Daniel; Floreano, Dario (2011). GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics, 27(16):2263-2270.

Abstract

Motivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks.
Results: Here we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic (ROC) curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international DREAM (Dialogue for Reverse Engineering Assessments and Methods) competition with three network inference challenges (DREAM3, DREAM4, and DREAM5).
Availability: GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual, and supporting data

Abstract

Motivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks.
Results: Here we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic (ROC) curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international DREAM (Dialogue for Reverse Engineering Assessments and Methods) competition with three network inference challenges (DREAM3, DREAM4, and DREAM5).
Availability: GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual, and supporting data

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141 citations in Web of Science®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2011
Deposited On:24 Jul 2013 11:18
Last Modified:07 Dec 2017 21:46
Publisher:Oxford University Press
ISSN:1367-4803
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/bioinformatics/btr373

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