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Gene Network Enrichment Analysis GNEA

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Aims to identify biological processes that are consistently deregulated across a broad set of microarray experiments associated with different disease models in both animal and human tissues. GNEA consists of five steps: (1) Assemble a collection of gene sets associated with biological processes or signalling pathways of interest (2) Assume an underlying model of cellular processes using a global protein–protein interaction network (3) Evaluate the hypothesis that genes in a given gene set are observed in a higher proportion (i.e., enriched) than expected by chance in the high-scoring subnetwork (HSN) and repeat for each gene set in the assembly (4) Order the gene sets of interest based on the number of different HSNs where they appear enriched (5) For each gene set, assign a p-value to the number of conditions where it is enriched.

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GNEA classification

GNEA specifications

Software type:
Framework/Library
Restrictions to use:
None
Programming languages:
R
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux
Computer skills:
Advanced
Maintained:
Yes

GNEA distribution

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No versioning.

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Publications

Institution(s)

Department of Biomedical Engineering, Boston University, Boston, MA, USA; Department of Cardiology, Children’s Hospital, Boston, MA, USA; Children’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA; Harvard-Partners Center for Genetics and Genomics, Boston, MA, USA; Center of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Center for Advanced Genomic Technology, Boston University, Boston, MA, USA

Funding source(s)

This work was supported by National Science Foundation grant number ITR-048715 and National Human Genome Research Institute grant number R01 HG003367-01A1, the National Institute of General Medical Sciences grant number K25-GM67825, the National Institute of Diabetes and Digestive and Kidney Diseases DGAP grant number TO1DK60837-01A1, and by the National Institutes of Health National Center for Biomedical Computing grant number 5U54LM008748–02.

Link to literature

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