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Incorporating the observed dominant species inthe predictions improved the root mean squared errors by 13.3 16.4 % and 12.6 28.9 % based on MSN and SUR, respectively, depending on the species.
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Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty.
Adding methylation at those two CpGs into a model that included the variables of the Systematic Coronary Risk Evaluation chart for fatal cardiovascular risk prediction improved the predictive discrimination.
The Class-C prediction improved the overall accuracy of the calculations and could serve as a reference for future study.
Mortality prediction improved the day following ECMO initiation, when ECMO has reduced acute stress and provided time to rest.
Graphical abstract The inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.
Phylogenomic inference has been shown to reduce the systematic errors associated with function prediction by homology; integration of structural information (or prediction) improves the accuracy of this approach.
Furthermore, we observed that the quality of the predictions improves as the number of genomes used for training the method increases, promising enhanced performance as more complete genome sequences become available.
Median gene centering of the UNC337 dataset before PAM50 or SCMGENE predictions also improved the survival classifications (Supplemental Fig. 1).
Incorporating histone signature information successfully penalized false prediction and improved the whole performance.
If the compounds with unknown interactions are removed from the analysis, the quality of the predictions improves.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com