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Literature derived gene and protein networks, obtained by manual curation based on published literature, are a popular way to interpret differentially expressed genes following drug treatment and to identify potential pathways and molecules targeted by the drug.
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At the cellular level, auxin is interpreted differentially in a tissue- and dose-dependent manner.
We interpreted differentially expressed probe-sets identified in the second analysis as being differentially regulated between the two groups of subjects.
Despite the characteristic directional changes, all treatments resulted in a similar [DA]4p/[DA]1p ratio, which may relay similar information regarding the frequency of firing rates of dopaminergic neurons; however, these directional variations may also be interpreted differentially post-synaptically.
Interpreting differential gene expression by "projecting" sub-networks significantly enriched with differentially expressed genes onto the Atlas of Signaling in order to identify key regulatory proteins and pathways involved in the differential response.
The Gene Ontology (GO) database [17] was used to further interpret the differentially expressed gene data set and to identify over-represented functional groups of genes.
IPA was used to interpret the differentially expressed proteins in terms of an interaction network and predominant canonical pathways.
Bioinformatics analysis was subsequently performed to interpret the differentially expressed genes of our FSL and RSL libraries.
The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective.
The integration of miRNA and mRNA expression analyses as well as network analysis enabled us to interpret the differentially-regulated genes from a systems perspective, yielding new insight into several biological pathways underlying phenotypic differences.
GSE was originally developed to biologically interpret lists of differentially expressed genes derived from microarray studies (Curtis et al., 2005) in terms of particular biological functions, processes or pathways [e.g. Gene Ontology (GO) (Ashburner et al., 2000) or KEGG Pathways (Kanehisa and Goto, 2000)].
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