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Exact(2)
Moreover, we filter for GO subontology root terms to exclude molecular function, biological process and cellular component.
To use this as a normalization, first, we filter for proteins in the the top five percent of local degree, eigenvector centrality and betweenness.
Similar(58)
Therefore, we filtered for these subspaces and selected again a group of similar subspaces (as shown in the video).
We filtered for pixel saturation and for consistency among replicates.
We filtered for probe sets expressed above background levels and retained 9377 genes for analysis.
Second, we filtered for SNPs for which δ ≥ 0.6 between CEU and YRI.
From the SSAHA2 output files, we filtered for alignments ≥ 30 bp.
Then we filtered for SNVs shared by at least two obese pools with the highest yield.
Tp perform GWAS, we filtered for rare alleles and missing data and obtained a5,795 SNPs set.
We filtered for possibly pathogenic homozygous variants that might cause recessive diseases.
First, we filtered for variants with a depth of 5 × and mapping quality (MQ) ≥20.
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CEO of Professional Science Editing for Scientists @ prosciediting.com