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To determine if the age-induced gene expression alterations comprised specific biological programs, we used gene set enrichment analysis (GSEA) to determine the statistical associations of predetermined gene cohorts.
Finally, we used gene set enrichment analysis (GSEA) to compare the differentially expressed genes in Metfl/fl Alb-Cre+/− regenerating livers against a gene set which was previously identified as G2/Metfl/fl Alb-Cre+/in synchronized HeLa cells [17].
To more systematically identify genes that were correlated with specific phenotypes, we used gene set enrichment analysis (GSEA), a computational method to determine the expression of genes associated with specific empirically determined phenotypes.
We first identified genes that were differentially expressed between skin and lung fibroblasts, between groups X and Y in matrix B, and between groups X and Y in matrix C. We then used gene set enrichment analysis (GSEA) [25] to identify gene sets enriched within each comparison.
Due to the small sample size of our data sets, we used gene set permutation for the GSEA analysis.
We used gene set enrichment analysis (GSEA) to examine the correlation between these a priori-defined gene sets and chemotherapy response [ 5].
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A similar type of analysis focusing on upregulated genes was carried out using gene set enrichment analysis (GSEA).
These profiles were then dissected analytically using gene set oriented techniques and complementary data from protein interaction databases.
We also confirmed the results using gene set enrichment [15] and set-based analyses [17] to uncover sets of functionally related genes showing evidence for association with disease.
This was followed by an analysis of our data at the level of gene sets (using gene set enrichment and bicluster analyses) in an attempt to discern potentially novel gene interactions that are impacted by loss of nAChR activity.
The data were also analyzed for gene set enrichment using gene set enrichment analysis (GSEA) [ 28].
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