Exact(7)
The normalization of data obtained from hybridization experiments with DNA chips to determine mRNA expression and concentration (gene expression profiling) is an unsolved problem.
The normalization of data was performed using RMA algorithm in the bioconductor package of R language that consists of background correction, normalization and summarization of the signal values [28], [29].
The normalization of data was achieved by the robust multi-chip average (RMA) procedure [ 51].
The average of fluorescence ratios from autosomal controls was used in the normalization of data in each hybridization experiment.
Apart from general considerations regarding the normalization of data in biology, special considerations apply to the normalization of quantitative real-time polymerase chain reaction (PCR) data [ 4].
To correct for the inner filter effect and for the normalization of data, the absorbance of FL buffer, free ligand in FL buffer, and reaction mixture were measured.
Similar(53)
Ion detection, clustering and the normalization of data-independent, alternate scanning LC-MSE data have been explained in detail elsewhere [ 27, 28].
After importing each of the other sets, the normalization of descriptor data should be done using training set data and then predictions can be made.
Consequently, the normalization of a data set in existing frameworks is affected by all of the data sets being processed at the same time.
Therefore, similar to the normalization of microarray data, quantile normalization on the multiplex RT-PCR results was performed.
We used two endogenous genes for the normalization of the data.
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