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Ellwanger, D.C. ; Leonhardt, J. ; Mewes, H.-W.

Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.

Nucleic Acids Res. 42:e166 (2014)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η(2) (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Messenger-rna; Transcription Factors; Target Interactions; Microrna Targets; Mirna Promoters; Binding Sites; Human-disease; Start Sites; Host Genes; Database
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Zeitschrift Nucleic Acids Research
Quellenangaben Band: 42, Heft: 21, Seiten: , Artikelnummer: e166 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)