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The microarray dataset, analyzed in this work, was provided by a study comparing the normal and the CML hematopoietic stem/progenitor cells in gene expression [ 27].
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The microarray datasets analyzed in this article are available in Gene Expression Omnibus, accession number GSE80748.
For example, Rhodes et al. [ 11, 12] combined results from four prostate cancer microarray datasets analyzed on different platforms.
In addition, using limited microarray datasets analyzed in this paper, the variation in DDX5 levels was also relatively small in other cancer cell lines, raising the possibility that DDX5 could serve as a novel internal control.
AS collected leaf samples, performed microarray lab work for the POP2 dataset, analyzed all microarray data and drafted the manuscript.
The resulting Affimetrix microarray dataset was analyzed by using the R environment (http://www.R-project.org) [82] and Bioconductor software (http://www.biocinductor.org).
For the null distribution, 1,000 sets of 158 genes randomly selected from the same microarray dataset were analyzed.
Relative expression values of PDGF family members were combined with a cDNA microarray dataset and analyzed based on clinical findings and PDGF expression patterns.
A real microarray dataset is analyzed, and the Gene Ontology Term Finder is used to evaluate the biological meaning of the resulting clusters.
Methylation data of 1,284 immune-related genes and 1,038 cell cycle-related genes from Gene Ontology (GO) and 575 genes from a dataset of stably expressed genes (genes with consistent expression in different physiological states and tissues) were extracted from a microarray dataset and analyzed using bioinformatics tools.
Sparse linear programming (SPLP) [ 10] represents a third approach which has been applied to a large microarray dataset derived analyzing from liver gene expression of compound-treated rats.
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