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To identify a common metastatic signature in solid tumors, we implemented a modified permutation counting method in the R statistical environment in order to perform a meta-analysis of 18 publicly available expression microarray datasets extracted from the Oncomine database (Table 1).
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PathRanker provides all functions to process the KEGG metabolic network, overlay a microarray dataset, extract the K most-correlated metabolic pathways, and analyze the key functional components within these pathways.
DYW performed the microarray dataset extraction and statistical analyses.
The protein dataset was further compared to the microarray dataset by extracting the GI numbers from the protein results.
*data extracted from microarray datasets; # data not exist in the dataset.
For both microarray datasets, the gene expressions were extracted from the.
We compared the core set of 522 DEGs of RNA-Seq with DEGs extracted from microarray datasets.
When comparing the gene expression profiles extracted from publicly available microarray datasets, some of the polyadenylation related genes showed tissue-specific expression, suggestive of potential different polyadenylation complex configurations.
Time series data were extracted from the available miRNA and mRNA microarray datasets, averaged across replicates at each time point, centered and scaled by their maximum absolute value (see Methods).
We used a modified version of the Markov clustering algorithm to systematically extract clusters of co-regulated genes from hundreds of microarray datasets stored in the Gene Expression Omnibus database (n = 1,484).
The purpose of this study was to extract and evaluate such seasonal gene expression characteristics from the existing multiple microarray datasets using the goldfish model.
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