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The goal is to convert M into a state such that the fragments (rows of M) can be distributed into two sets corresponding to the two haplotypes.
Like the valence density of states, the core levels are now distributed into two sets, corresponding to charged and neutral molecules.
The available data was distributed into three different sets for the training method, i.e., 70% for the training set, 15% for the validation set and remaining 15% for the test set.
To be a little more specific, let us suppose that all the records of a dataset are distributed into two equal sets, say A and B, and that furthermore we create three more sets: for matched pairs, for non-matched pairs and a set of possible matched pairs.
Moreover, Table 3 shows the dependence of the maximum absolute error on the cardinality of the matched pattern set (#MP), distributed into three possible ranges; for example, the last value of the first row means that among all the 15-min-ahead forecasts with a population of matched patterns larger than 30, the maximum absolute error was found to be 0.00759.
Our series included 161 cases that were distributed into two different tumour sets.
The animals were distributed into three groups.
Animals were randomly distributed into three groups of four pigs.
The patients were randomly distributed into three groups.
Rats were distributed into three groups: control, steatosis and resveratrol.
The relative TNFAIP3/CEP6 signal ratio was distributed into three peaks.
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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