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Fold changes in gene expression were determined using the ΔΔCt method with normalization to 18S rRNA endogenous control.
Samples were run in triplicate and transcript levels were calculated according to the ΔΔct method with normalization to CyclophilinB.
The relative HNF4α RNA expression levels were estimated using the ΔΔCt method with normalization to mouse GAPDH RNA.
Data analysis was based on the ΔΔCt method with normalization of the target genes in MBP-1 knockdown fibroblasts to control fibroblasts.
The level of expression of each tested gene was calculated using the ΔCt method with normalization to the RPL19 housekeeping gene.
Data were imported into an Excel database and analyzed using the comparative cycle threshold method with normalization of the raw data to housekeeping genes including β-glucuronidase, hypoxanthine guanine phosphoribosyl-transferase1, β-actin, Hsp90 and glyceraldehyde-3-phosphate dehydrogenase.
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The information was extracted from the features close to the background or saturated and normalization was performed by the rank invariant and lowest fitness method with spatial normalization.
To account for varying intensity levels and other variability between different scans, we normalized the 225 chips using the Robust Multichip Average method with quantile normalization of the log2 intensities with prior GC correction (GC-RMA [ 40, 41]), using the mean of all probes associated with the given gene.
Our method with individual normalization has better average classification accuracy than the approach presented in [1].
A least-square algorithm method with appropriate normalization is used for solving the over-determined system of equations with noise-polluted data.
It can be seen that the mean rank obtained by the proposed methods outperform all other methods except the one discussed in subsection 5.1.4 (separability method with cohort normalization).
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