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The genes that were not included in these datasets were removed from the gene set for the given pathway.
To overcome sampling bias between GOS datasets, poorly sequenced datasets were removed (e.g. GS113: 718 reads, GS114: 9741 reads) and each remaining dataset was randomly resampled to the size of the smallest remaining dataset (GS120 - 46,052 sequences) using the daisychopper (http://www.genomics.ceh.ac.uk/GeneSwytch/Tools.html) tool.
Further evidence for this was given by the comparable performance of both platforms for overall samples and those in each subclass shown in Figure S2, where the 100 control datasets were removed and only the 318 treated samples were retained in the AFX dataset.
Additionally, redundant probe sets representing genes found to overlap between datasets were removed from the Se toxicity set.
Batch effects within and between the CCLE, CGP, and NCI60 datasets were removed using the ComBat function from the R 'sva' package [ 10].
Furthermore, redundant patients present in KJX64/KJ125 and Uppsala datasets were removed from the validation tests so they were only considered once.
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This has resulted in a significant policy change, with some existing datasets being removed from the internet.
High levels of noise inherent to most biological datasets are removed first, and the true signal is further amplified for enhanced data interpretation.
Incomplete cases in the remaining dataset were removed.
To remove any bias during benchmarking, the ligands found in the Vernalis dataset were removed from the PDB ligand library.
To construct a phylogenetic tree in MEGA4 [26], nifH sequences were first clustered at 92% similarity (complete linkage clustering) and clusters with <10 sequences (together representing 0.9% of the dataset) were removed for clarity.
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