In this study we partitioned the patients with lung adenocarcinoma into two groups, one with a wide-type TP53 gene and the other with somatic mutations in the TP53 gene, and constructed gene co-expression networks for the two groups. From the comparative analysis of the two GCNs we obtained...
Wiley‐VCH Verlag GmbH & Co. KGaAWeirauch MT (2011) Gene Coexpression Networks for the Analysis of DNA Microarray Data. Appl. Stat. Netw. Biol. Wiley-VCH Verlag GmbH & Co. KGaA, pp 215-250Weirauch MT (2011) Gene coexpression networks for the analysis of DNA microarray data. In: ...
Gene co-expression networks (GCNs) are transcript–transcript association networks, generally reported as undirected graphs, where genes are connected when an appreciable co-expression association between them exists. From:Encyclopedia of Bioinformatics and Computational Biology,2019 ...
Gene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these...
We reconstructed and compared sporulation gene co-expression networks (GCNs) of the model laboratory strainBacillus subtilis168 and the food-borne industrial isolateBacillus amyloliquefaciens. Transcriptome data obtained from samples of six stages during the sporulation process were used for network inference...
The pipeline contains three steps: (1) Determining the cutoff values, (2) constructing eight GCNs for different coexpression types, and (3) determining time-ordered levels for nodes in a GCN of interest. Prepare the gene expression data ...
We then used Gene Oracle to classify the input brain samples with these mini-GCNs to test their biomarker potential on normal brain regions. Finally, we tested if the brain region-specific genes tumor expression profiles were able to discriminate the brain from non-brain human tumors. Figure 1...
Two public cancer lnCaNet co-expression networks that were constructed using TCGA data (‘lncanet_BLCA_Cancer’ and ‘lncanet_OV_Cancer’), were downloaded for comparison59. In addition, protein-protein interaction data from BioGrid that is included in the EGAD R package was also used. Gene ...
Gene co-expression networks (GCNs) are graphic representations where nodes symbolize genes while edges reconstruct the coordinated transcription of genes to certain external stimuli. In this paper, an enhanced novel methodology for construction and comparison of GCNs is proposed. Microarray datasets from ...
A key component of some of these models is the use of more complex machine learning operations such as graph-structured data to develop “graph convolutional networks” (GCNs; [54]), which can produce representations that encode both local graph structure (connectivity) and features of nodes, ...