GeNGe: systematic generation of gene regulatory networks Export

@article{citeulike:4176602, abstract = {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. Availability: Available online at http://genge.molgen.mpg.de Contact: hache@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp115}, author = {Hache, Hendrik and Wierling, Christoph and Lehrach, Hans and Herwig, Ralf}, booktitle = {Bioinformatics}, citeulike-article-id = {4176602}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp115}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/25/9/1205.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/25/9/1205.full.pdf}, citeulike-linkout-3 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/9/1205?etoc}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/19251773}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=19251773}, day = {1}, doi = {10.1093/bioinformatics/btp115}, issn = {1367-4811}, journal = {Bioinformatics (Oxford, England)}, keywords = {gene\_network, gene\_regulatory\_networks, network\_topology, networks, pathways}, month = {May}, number = {9}, pages = {1205--1207}, posted-at = {2009-11-10 22:31:25}, priority = {2}, title = {GeNGe: systematic generation of gene regulatory networks}, url = {http://dx.doi.org/10.1093/bioinformatics/btp115}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:4176602 L3 - citeulike-article-id:4176602 N2 - 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. Availability: Available online at http://genge.molgen.mpg.de Contact: hache@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp115 IS - 9 JF - Bioinformatics (Oxford, England) SN - 1367-4811 EP - 1207 TI - GeNGe: systematic generation of gene regulatory networks VL - 25 T2 - Bioinformatics SP - 1205 KW - gene_network KW - gene_regulatory_networks KW - network_topology KW - networks KW - pathways AU - Hache, Hendrik AU - Wierling, Christoph AU - Lehrach, Hans AU - Herwig, Ralf PY - 2009/05/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btp115 ER -