Your English writing platform
Discover LudwigSuggestions(1)
Exact(60)
For gene ontology analysis, genes were grouped into sets based on Gene Ontologies (GO).
Genes were grouped by dynamics using an intersection of results based on differential expression analysis.
Essential gene comparisons were based on sequence homologs, and genes were grouped according to pathways or functions.
All tested TGMS lines controlled by 3 tgms genes were grouped together by cluster analysis into one sub-group of Indica.
For this analysis, genes were grouped based on functional pathways.
Using the accompanying expression data in [8], genes were grouped into high expressing and low expressing.
Differentially expressed genes were grouped according to self-organizing maps (SOM) (Figure 3 and Table S4).
As shown in Figure 4, Panel A, up-regulated genes were grouped into four major clusters: labeled A D.
Genes were grouped according to the role category annotation in CMR-JCVI L. monocytogenes EGDe genome database (http://cmr.jcvi.org).jcvi.org
To analyze the microarray data, genes were grouped based on their expression vectors using the k-means algorithm.
Gene expression profiles of all genes were grouped by hierarchical clustering (TIGR MeV v.4.5.1; Manhattan distance, average linkage).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com