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For example, brown module genes clearly participate in photosynthetic processes while turquoise module genes contribute to starch and sucrose regulation.
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The path module corresponds to a real pathway where the genes contribute to a signal transduction.
To assess these transcription patterns and understand what genes contributed to these patterns, we used WGCNA, statistical analyses of eigengene values, and GO enrichment analyses to identify/characterize gene modules with specific phenotype × diet patterns.
Fisher's exact test shows that cancer methylated genes has significantly enriched distribution in Module One (P = 0.006), suggesting that genes in Module One contribute more to the long-term loss of gene expression and represents the early stage of tumor formation[1].
A key contribution of this work is the integration of differential expression analysis, gene-trait correlation analysis, and gene network analysis to identify both the coexpressed gene modules that contribute to insulin sensitivity and the key causal regulators.
The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells.
We randomly selected 200 genes from the 4000 genes to define module genes of each expression modules.
The first measure of module significance is simply the average gene significance measure across module genes.
We defined a measure of module significance as average absolute gene significance across the module genes.
In each set, we obtained the number of modules, the number of significant modules, and the number of module genes in the significant modules (Table 1).
This module was tested by simulated activation and knockdown of individual module genes.
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