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Recent research in the protein intrinsic disorder was stimulated by the availability of accurate computational predictors.
Computational predictors of MHC binding are often used within in silico vaccine design pathways.
In this study, the computational predictors and traditional experimental predictors were compared with human subsequent recall.
It was found that the computational recall of the resultant network is significantly correlated with human memory recall performance, while other computational predictors without theta phase precession are not significantly correlated with subsequent memory recall.
In the second analysis (Section 3.2), the prediction abilities of the traditional experimental predictors (shown in Section 4.3) were further evaluated and compared with the prediction ability of the computational predictors.
This property is completely different from other computational predictors
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This motivated us to develop a computational predictor of metal ion sites for RNA structures.
Currently, there is no widely accepted computational predictor in clinical use for evaluating uncertain gene variants.
In both cases the causal link between the computational predictor and the biological function is not fully understood yet both have been fairly successful at predicting origin locations in their respective species.
This situation motivated us to develop a computational predictor which only uses information about the RNA structure to identify the most likely metal ion-binding sites in this structure.
Computational site predictors provide valuable information for function annotation and they are also useful to guide and accelerate mechanistic, ligand-binding and protein engineering studies.
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