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We may assume that the data are labelled in a non-descending order, i.e. that x i ≤ x i + 1.
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Col/Col homozygous data are labelled and highlighted with an arrow in each plot.
On the other hand, 11 PGx variants from current data are labeled as disease in the same database, suggesting some overlap between disease and PGx variants.
In the bipartite model, the known interactions in the training data are labeled as +1 while all other unknown drug-protein pairs in the training data are assumed as non-interactions with label 0. Then three different classifiers are available: new drug candidate versus known target protein, known drugs versus new target protein and new drug candidate versus new target protein candidate.
These hybrids represent intra- and inter-heterotic group crosses with a range of low to high genetic diversity between the parents and exhibit a substantial range of BPH phenotypes (the data points for these six hybrids are labelled in Figure 3).
The data were labelled with serial numbers and analysed in a manner that protected patient privacy.
As such data was labeled 'missing' in the present study, the reported results likely underestimate the true discriminant validity of the Mindstreams tests.
The data were labeled with serial numbers and analyzed in a way to protect patient privacy.
Missing data were labeled as unknown and classified separately in most of the analyses undertaken.
Then, this in silico data is labeled as experimental and we use the proposed method to estimate the model parameters.
The most discriminating variables from Table 1 (social withdrawal, history of confusion, objective fever, oral candidiasis, coma, CD4 < 100/μl) gave the highest J scores (i.e., sensitivity + specificity - 1; data not shown) and are labeled in Figure 1.
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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