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The expectation is that short-listed ncSNVs will be enriched with pathogenic variants, and indeed greater than 94% of ncSNVs in the top ranked predictions were from the pathogenic set when the test datasets consisted of non-pathogenic and pathogenic variants in equal numbers (mixing ratio = 1 1) (Fig. 3a).
Genomic predictions were from ridge regression or a Bayesian analysis.
Functional predictions were from Generic GO Term Finder (http://go.princeton.edu/cgi-bin/GOTermFinder).
Summing over all of the chemoreceptor families, about one-third of the best protein predictions were from brigpep and the rest were from GeneWise.
About one-quarter of the best protein predictions were from wum, about one-tenth were from genefinder, and the rest were from GeneWise.
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These predictions are from Dr. Steven K. Feiner, a professor of computer science at Columbia University and an expert on wearable computers.
Deviations from IBL flux predictions are from wind streamline compressions up slopes, leading to a speedup effect.
They can see how far off their predictions are from reality by checking against the omniscient data set.
Importantly, a high number of false-positive predictions were made, with 31% of CFS-positive predictions being from healthy volunteers.
The calibration plots describe how far predictions are from actual outcomes and how the prediction model fits the data.
The formal prediction was from the period 08 00 to 18 45 local Manila time (00 00 to 10 45 UTC).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com