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The top 10% candidates in a QTL candidate set prioritized by a method were termed as winners for that method, e.g., RWR winners were top-ranked genes by the RWR method.
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The application in our context is very similar: the datasets are the judges and the Macro-F1 achieved by a method is the rating from the judges.
In general, the number of genes predicted by a method are always a balance between false positives and false negatives.
Under this testing framework, the prediction made by a method is measured at the nucleotide, binding site and motif levels.
The latter useful statistic, quantifying the proportion of SNPs missed by a method, is not always reported; confirmation rates of up to 97% in soybean and rice were reported by [ 29], but without mention of the accompanying false-negative rate.
In order to quantitatively measure how close a population distribution produced by a method was to the true one, we used a divergence measure based on Shannon entropy, called L-divergence (Lin, 1991).
Such a method was adopted by Takeuchi and Hirashita (2000).
Light-emitting 5,6,11,12-tetraphenylnaphthacene (rubrene) nanoparticles (NPs) prepared by a reprecipitation method were treated hydrothermally.
First, well-dispersed Fe3O4 microspheres prepared by a solvothermal method were used as the sacrificial template and the magnetic precursor.
A manually identified position that was overlooked by a computational method was called a false negative.
Anything that can be produced by a simple method is by definition orderly.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com