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With the subtype classification, we generated ranked gene lists for each subtype as discussed in the Methods section (see Table 4 for the statistics).
GSEA (19) was performed on the generated ranked gene list using an E2F1-upregulated gene set generated from the publically available data set (accession number GSE1562; http://www.ncbi.nlm.nih.gov/geo/) consisting of control (LacZ) and exogenous E2F1 expressing melanoma SK-MEL-2 cells (20).
GO analyses performed on the generated ranked gene lists displayed overall cluster enrichment score (log of geometric mean of EASE scores of all GEO terms included in the functional cluster) and both EASE score (modified Fisher exact test ; ref. 25) P values for GEO term enrichment and Bonferoni corrected P values to account for multiple testing.
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Alternatively, enrichment-based analysis [4], [20] [22] can be applied to such a gene list to generate ranked functional categories (e.g., GO) or pathways based on their enrichment levels, so that the significantly enriched pathways and their associated genes can be easily identified as the primary biological themes.
mRNAs were then ranked according to the Pearson correlation coefficient to generate ranked gene lists for GSEA.
Most statistical methods aim at generating ranked lists of single genes which are differentially expressed according to a certain level of significance.
This stage generates ranking based on purely scientific factors.
SVM-RFE generates ranking of features by computing information gain during iterative backward feature elimination.
The obtained fitness value is simply a quality assessment of the generated ranking.
Each missing rank was then replaced with the arithmetic mean of five synthetically generated ranks.
We generated rank ordered lists of enzyme pairs based on the local methods.
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