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We observed that the RT-PCR data had lower stochastic variance than the microarray data (estimated using replicate measurements in the RT-PCR analysis and from bootstrap analysis of the microarray data).
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Correlation between qPCR and microarray data was estimated by Spearman's Rho.
In order to validate the microarray data, we estimated the CD44 levels in treated versus untreated cell lines.
Applying this algorithm to real microarray data, we estimated haplotype frequencies and diplotypes in 1486 CNV regions for 100 individuals.
In general, the microarray data under estimated the extent of up or downregulation compared to the qRT-PCR for approximately 50% of the genes analyzed.
Agreement of the individual studies raw microarray data was estimated by the Integrated Correlation Coefficient Analysis, which produces the general Integrated Correlation Coefficient (ICC), representing agreement between studies, and can be interpreted in the same way as Pearson correlation coefficient.
However, one can use the massively parallel structure of microarray data to estimate an empirical Bayes estimate of the posterior probability.
By training these parameters and through the extensive use of multiple genomes, combined with Gene Ontology filters or microarray data, we estimate that genome-wide discovery of TGs is feasible for about 50% of the TFs tested.
The Coexpression database (COXPRESdb) version 5.0 [ 22] is freeware of bioinformatics that contains the collection of comprehensive information on gene coexpression retrieved from large sets of public microarray data to estimate gene functions of 11 species, including Homo sapiens, M. musculus, R. norvegicus, D. melanogaster, and C. elegans[ 23].
Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated.
It has been well-known in microarray data analysis that the estimated standard deviation is probably unstable when sample size in each group is small.
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