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In order to validate theoretical predictions, a set of reactive distillation experiments is performed in a glass tray column with 80 bubble cap trays.
The aim of this work is to derive from interface predictions a set of optimal restraints for data-driven docking.
The aim of this work is to derive from interface predictions a set of optimal restraints for data-driven docking using HADDOCK.
To validate these predictions, a set of fine-mapped candidate genes was obtained from the literature.
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Analysis of several variations of the algorithm demonstrates that this approach can reduce false positive minimotif predictions in the test dataset and eliminate many predictions in a set of randomly selected query proteins.
Starting from the known ligand binding site, we predicted the function type of each sub-cavity and compared these predictions to a set of known binding ligands.
In the fusion, the bIPDA track predictions form a set of measurements at scan k.
It automatically and simultaneously generates both individual secondary structure predictions within a set of homologous RNA genes and a consensus structure for the data set.
In the present analysis, we provide robust evidence that combining multiple single-gene predictions produced a set of targets that could be validated at several levels.
We expected to generate plausible predictions for a set of effective combinations and to be able to rank their relative importance, to guide validation experiments.
The output of the model is a prediction of a set of TFBSs which are being predicted, ranked in order of decreasing likelihood.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com