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3) Can a compact feature set achieve good diagnostic performance?
However, the most common applications of the RDP Classifier and training set achieve only resolution at best to the genus level.
As shown in the previous section, two equivalent models (based on two Markov boundaries) developed in NARAC training set achieve predictive accuracy 0.81 AUC (95% confidence interval: [0.78; 0.84] AUC) in NARAC testing set.
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A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model.
Note, that the sequence-based method, NetMHCpan-1.0, evaluated using a leave-one-allele-out approach on the same data set, achieved a performance of 0.674.
The best random classifier for the data set achieved a classification accuracy of 51.1%.
However, training was terminated early when the monitoring data set achieved its minimum rmsd.
All filters in the validation set achieved 91.1-96.491.1-96.4%ity, 96.0-98.6% specificity, and 95.9-98.5% accuracy, however the precision dropped to 28.5-52.928.5-52.9%
We then counted the number of times each gene set achieved top rank in the one thousand permutes.
However, the response achieved from all backgrounds is consistent with the demographics of the cohort and analysis across the data set achieved thematic saturation.
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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