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GGMs tend to have been used on expression data from small specifically designed experiments where important factors have been perturbed, and there is not enough data to consider a Bayesian network.
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Our evaluation of several commonly used distance measures on expression data of three different species draws a fairly consistent picture.
Simple clustering methods such as agglomerative hierarchical clustering and k-means have been widely used on gene expression data analysis.
In this work, we have focused on the use of expression data from ENCODE, which include matched mRNA and miRNA expression data from the same cell lines.
After translating probe intensities into genetic traits of gene expression and SNPs, statistical analysis was then performed on expression data using significance analysis of microarrays (SAM) to select differentially expressed genes.
We demonstrate the use of our method on expression data from clear cell Renal Cell Carcinoma (ccRCC) and matched normal kidney samples.
To elucidate the impact of limited quenching on expression data, comparative experiments using Escherichia coli cell lysate were performed in this work.
We used gene expression data on prostate tumours and controls from four studies [ 38- 41].
The first algorithm has been used on three gene expression data sets (yeast cell cycle data, human fibroblast response to serum data and the cutaneous melanoma data) from the open literature, while the second has been used on the fibroblast data set.
Importantly, we did not base our analysis on single measurements of gene expression, nor did we focus on individual genes; rather, we used gene expression data from biological replicates to rule out biological variability (especially between bacteria recovered from different mice) and to look for changes in gene clusters, operons, and pathways.
In a simple case this is used to display expression data on top of a gene network but based on using query results as view items it can also be used to display the number of publications associated with a gene - phenotype association.
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CEO of Professional Science Editing for Scientists @ prosciediting.com