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A change in paired data was detected using Wilcoxon matched-pairs signed-rank test.
For each regression model, autocorrelation of the data was detected using the Durbin-Watson test and corrected using general least squares regression [ 25, 6].
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CNVs in the genome data were detected using RDXplorer [ 41] and filtered with a customized pipeline.
Non-homogeneities in the data were detected using residual analysis as described elsewhere [ 27]. 2 × 2 crossover ANOVA was used for data evaluation.
Outliers in microarray data were detected using criterion of z > 7 for log-transformed Cy3 and Cy5 data separately and removed (0.17% of the data).
The present genes (represented by probe-sets of Affymetrix Gene Chips®) of our microarray data were detected using Affymetrix MAS5 method implemented in Bioconductor packages.
SSRs in the read data were detected using a Perl script in the Simple Sequence Repeat Identification Tool (SSRIT), with minimum thresholds of nine, six, five, five, five, and five repeat units for di-, tetra-,etra-, penta-, hexa-, and heptanucleotide repeats, respectively.
Based on morphological classification only 5%% of the individuals were classified as introgressed individuals, which was much less than what was detected using genotypic data.
In the study of Shen and Qin (2012) a p.V600K mutation was overlooked by visual inspection but was detected using pyrosequencing data analysis software [ 38].
The dimension reduced data was divided by SFC algorithm as high-risk and low-risk data, which are detected using different frequencies to achieve enhanced detection efficiency and accuracy.
A basic 2 × 2 MIMO receiver applies interference suppression in a way that first the stronger data layer is detected using the MMSE and thereafter the detected data layer is canceled from the received data before detecting the second layer by ordinary maximal ratio combining [1].
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