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GeNGe: systematic generation of gene regulatory networks
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Zeitschriftentitel: | Bioinformatics |
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Personen und Körperschaften: | , , , |
In: | Bioinformatics, 25, 2009, 9, S. 1205-1207 |
Format: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Oxford University Press (OUP)
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Schlagwörter: |
author_facet |
Hache, Hendrik Wierling, Christoph Lehrach, Hans Herwig, Ralf Hache, Hendrik Wierling, Christoph Lehrach, Hans Herwig, Ralf |
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author |
Hache, Hendrik Wierling, Christoph Lehrach, Hans Herwig, Ralf |
spellingShingle |
Hache, Hendrik Wierling, Christoph Lehrach, Hans Herwig, Ralf Bioinformatics GeNGe: systematic generation of gene regulatory networks Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
author_sort |
hache, hendrik |
spelling |
Hache, Hendrik Wierling, Christoph Lehrach, Hans Herwig, Ralf 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp115 <jats:title>Abstract</jats:title> <jats:p>Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments.</jats:p> <jats:p>Availability: Available online at http://genge.molgen.mpg.de</jats:p> <jats:p>Contact: hache@molgen.mpg.de</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> GeNGe: systematic generation of gene regulatory networks Bioinformatics |
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10.1093/bioinformatics/btp115 |
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Oxford University Press (OUP) |
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Bioinformatics |
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GeNGe: systematic generation of gene regulatory networks |
title_unstemmed |
GeNGe: systematic generation of gene regulatory networks |
title_full |
GeNGe: systematic generation of gene regulatory networks |
title_fullStr |
GeNGe: systematic generation of gene regulatory networks |
title_full_unstemmed |
GeNGe: systematic generation of gene regulatory networks |
title_short |
GeNGe: systematic generation of gene regulatory networks |
title_sort |
genge: systematic generation of gene regulatory networks |
topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
url |
http://dx.doi.org/10.1093/bioinformatics/btp115 |
publishDate |
2009 |
physical |
1205-1207 |
description |
<jats:title>Abstract</jats:title>
<jats:p>Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments.</jats:p>
<jats:p>Availability: Available online at http://genge.molgen.mpg.de</jats:p>
<jats:p>Contact: hache@molgen.mpg.de</jats:p>
<jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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author | Hache, Hendrik, Wierling, Christoph, Lehrach, Hans, Herwig, Ralf |
author_facet | Hache, Hendrik, Wierling, Christoph, Lehrach, Hans, Herwig, Ralf, Hache, Hendrik, Wierling, Christoph, Lehrach, Hans, Herwig, Ralf |
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description | <jats:title>Abstract</jats:title> <jats:p>Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments.</jats:p> <jats:p>Availability: Available online at http://genge.molgen.mpg.de</jats:p> <jats:p>Contact: hache@molgen.mpg.de</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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spelling | Hache, Hendrik Wierling, Christoph Lehrach, Hans Herwig, Ralf 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp115 <jats:title>Abstract</jats:title> <jats:p>Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments.</jats:p> <jats:p>Availability: Available online at http://genge.molgen.mpg.de</jats:p> <jats:p>Contact: hache@molgen.mpg.de</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> GeNGe: systematic generation of gene regulatory networks Bioinformatics |
spellingShingle | Hache, Hendrik, Wierling, Christoph, Lehrach, Hans, Herwig, Ralf, Bioinformatics, GeNGe: systematic generation of gene regulatory networks, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
title | GeNGe: systematic generation of gene regulatory networks |
title_full | GeNGe: systematic generation of gene regulatory networks |
title_fullStr | GeNGe: systematic generation of gene regulatory networks |
title_full_unstemmed | GeNGe: systematic generation of gene regulatory networks |
title_short | GeNGe: systematic generation of gene regulatory networks |
title_sort | genge: systematic generation of gene regulatory networks |
title_unstemmed | GeNGe: systematic generation of gene regulatory networks |
topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
url | http://dx.doi.org/10.1093/bioinformatics/btp115 |