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The microarray dataset obtained from these experiments contains expression levels for 22690 Affymetrix probe set IDs.
First, we used a microarray dataset obtained from the analysis of several human wild-type and cancer tissues [ 12].
To demonstrate our method, we applied our algorithm to a microarray dataset obtained from a progression and reversion series of breast cancer cells.
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We utilized multiple microarray datasets obtained from undifferentiated ESCs and differentiated EBs of human and mouse for cross-species examination of transcriptional co-expression.
In order to systematically relate breast cancer mesenchymalization to any clincopathologic parameters, we analyzed the A17-signature expression using two publicly available microarray datasets obtained from two independent studies carried out on human patients.
We next analysed LIMK2 gene expression from CRC microarray datasets obtained using Oncomine.
To screen the RNA-seq data for genes potentially essential for biofilm growth of B. cenocepacia J2315, we compared RNA-seq data with published microarray datasets obtained from cells grown in a biofilm [ 30], from planktonic cells harvested in stationary phase, and from cells grown under reduced oxygen levels [ 19] as well as under various stress conditions [ 19, 30].
TRAM was able to generate original results of relevant biological interest in the ab initio modelling of differentiation from CD34+ stem cells to megakaryocyte (Mk) cells in a meta-analysis of a total of 28 publicly available microarray datasets obtained from different sources.
The cassava microarray dataset was obtained from a previous study [41].
The working microarray dataset was obtained through gene filtering, background correction, logbase2 (log2) transformation, and normalization.
The transcriptome dataset obtained via microarray analysis was initially subjected to an unsupervised Principal Component Analysis (PCA).
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