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GeneNetwork is based on expression datasets from humans, mice, and rats, and predicts functions of genes against known pathways in various biological databases.
Since parallel miRNA/mRNA expression datasets are rapidly accumulated by the advance of next-generation sequencing techniques, cGRNB will be very useful tool for researchers to build combinatorial gene regulatory networks based on expression datasets.
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In the TMA patient dataset, we detected 2 groups of SCLC based on SYK/FYN expression alone.
An expression dataset of target genes was generated based on the fluorescence intensity output from the Roche NimbleGen platform.
While Cufflinks probabilistically assign reads to isoforms and is widely accepted as an accurate expression quantification method, one could focus on local differences, i.e., what exons are being included or excluded, and consider a custom set of isoforms based on expression within the working dataset.
The core set of regulated genes was divided into two groups for each dataset based on expression change; genes altered at least two-fold and genes altered less than two-fold under that specific germination condition.
Next, quantile normalization was applied separately to the log2 transformed expression values in each dataset.[18] Correlations between genes were calculated based on expression patterns across the diverse samples in both the human as well as the mouse datasets.
Based on three expression datasets for cancer study and the KEGG pathway database, we selected relevant genes and pathways, many of which are supported by biological literature.
In the previous study of gene-set analysis [ 29], a comparison scheme has been built to evaluate the performances of different enrichment analysis methods (e.g., ORA or GSEA) based on multiple expression datasets about complex diseases.
Based on gene expression microarray datasets, constructing differential co-expression networks (DCNs) is an important method to investigate diseases and there have been some relevant good tools such as R package 'WGCNA'DCGLCGL'DCGL
It is our belief that our web server will greatly help biologists to model and analyze conditional combinatorial gene regulatory networks based on their own expression datasets.
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