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This is a statistical effect only in the sense that a "true" dataset is filtered due to a detectability threshold.
In most of the cases, a small fraction of total examples in the training dataset were used to gather all the information required for generating the classification model, thus the original dataset is filtered to make more informative and representative dataset in terms of Support Vectors (SVs) of representative sequences.
Prior to analysis, the training dataset is filtered to exclude unannotated and lowly expressed genes, without regard to phenotypic information.
As can be seen in Figure 3B, the ConQuass score becomes progressively higher as the dataset is filtered to leave only structures whose alignment is of higher quality according to any one of the four measures.
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The population dataset was filtered in R using an iterative process to exclude samples and SNPs with excessive missing data.
The dataset was filtered based on posterior error probability to arrive at a false discovery rate below 1% estimated using a target-decoy approach86.
Initial dataset was filtered based on MAF >0.05 and 95% call rate.
The miRNA dataset was filtered according to the standard procedure to exclude spots with minimum intensity and size.
To identify unique features of individual depots, the dataset was filtered for processes with marked differences in enriched and differentially regulated genes.
The dataset was filtered to include only genes called present or marginal (>2 fold signal/background in either Cy3 or Cy5 channels) in at least 60% of hybridizations.
In order to objectively identify probe sets of interest, the entire dataset was filtered with criteria similar to the ones applied to previous gene array datasets [18], [19] and identical to the approach used with the liver array results [16].
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