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  TRANSWESD : inferring cellular networks with transitive reduction

Klamt, S., Flassig, R., & Sundmacher, K. (2010). TRANSWESD: inferring cellular networks with transitive reduction. Bioinformatics, 26(17), 2160-2168. doi:10.1093/bioinformatics/btq342.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-9094-5 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-8B9E-0
Genre: Journal Article

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493348_bioinform_2010.pdf (Publisher version), 204KB
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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 Creators:
Klamt, S.1, Author              
Flassig, Robert2, Author              
Sundmacher, Kai2, 3, Author              
Affiliations:
1Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738139              
2Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151              
3Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              

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 Abstract: Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic. Results: We present TRANSWESD, an elaborated variant of TRANSitive reduction for WEighted Signed Digraphs that overcomes conceptual problems of existing versions. Major changes and improvements concern: (i) new statistical approaches for generating high-quality perturbation graphs from systematic perturbation experiments; (ii) the use of edge weights (association strengths) for recognizing true redundant structures; (iii) causal interpretation of cycles; (iv) relaxed definition of transitive reduction; and (v) approximation algorithms for large networks. Using standardized benchmark tests, we demonstrate that our method outperforms existing variants of transitive reduction and is, despite its conceptual simplicity, highly competitive with other reverse engineering methods. Contact: klamt@mpi-magdeburg.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. [accessed 2013 July 2nd]

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Language(s): eng - English
 Dates: 2010
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: eDoc: 493348
DOI: 10.1093/bioinformatics/btq342
Other: 15/10
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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 26 (17) Sequence Number: - Start / End Page: 2160 - 2168 Identifier: -