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Table 4 shows the area under the roc curve values for VS methods and data fusion.
Then for different VS methods we obtain different predicted ranking lists based on different prediction models.
In short, these data sets were designed to be difficult for VS methods.
Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets.
Figure 5 a) It shows the enrichment plots for VS methods and b) shows the enrichment plots for data fusion.
Ligand-based VS methods need a biologically active structure that serves as a query in the experiment.
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The performance of SVM is substantially improved against several other VS method based on the same datasets and molecular descriptors, suggesting that the VS performance of SVM is primarily due to SVM classification models rather than the molecular descriptors used.
Since we did not find the Au catalyst on top of the nanowire, the growth mechanism for this core-shell structure may be through the vapor-solid (VS) method [14] or a combination of the VLS and VS methods.
None of these methods required any training set which was mandatory in a VS method.
The VS method has been used to solve the NLOS and multipath problem in [9 11].
Simulation results confirmed the robustness of our VS method in terms of RMSE and ALE.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com