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Data were labelled as MIAME compliant.
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Missing data were labeled as unknown and classified separately in most of the analyses undertaken.
All subjects were included in the analyses; when diaries were not completed, the data were labeled as "missing".
The duration of each call was automatically measured along with peak amplitudes and frequencies, and these data were labeled as occurring before, during, or after the CS.
Data was labeled as spam if it had a high cosine similarity with another instance while their model measures similarity between instances using semantic analysis.
Then, this in silico data is labeled as experimental and we use the proposed method to estimate the model parameters.
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 present data set, 3,373 protein sequences (10% of the data) are labeled as "known peptides", which means that these proteins were mapped to entries in Swiss-Prot, RefSeq or SPTrEMBL during the annotation process.
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.
For instance, isolates collected from October 1999 through April 2000 were labeled as year 2000 data.
For example, isolates collected from October 1999 to April 2000 were labeled as year 2000 data.
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CEO of Professional Science Editing for Scientists @ prosciediting.com