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The GHMRF approach reclassifies the voxels into recurrent patterns by maximizing the probability to observe the classification for the given tomogram.
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This suggestion was based on different experiments and observing the classification rate on JAFFE database.
For example, this research has observed the classification of roughly one-in-six noncompliant patients (16.4%, CI 6.6%too 26.1%) as exhibiting the characteristic of pseudo compliance.
In this sense, we can compare which feature is more relevant by adding, deleting and combining features and observing the classification results.
We considered different combinations of the feature types of the RM kernel (sequence windows, conservation information and positional information) and observed the classification performance.
The variables with areas under the ROC curve less than 0.5 were transformed into their inverses, in order to observe the same classification criteria with all the variables [ 20].
To observe the functional classification of DPGs in Dmel, we analyzed their annotations and included six other eukaryotic genomes, H. sapiens, M. musculus, G. gallus, C. elegans, S. cerevisiae, and A. thaliana for comparison [see Additional file 7].
Once again, we observe that the classification accuracy is generally improved by concatenating the distance-based features to the original feature.
We observed the same classification accuracy in predicting TNBC tumors (95%) when lasso, elastic net and Logit-Lapnet were applied to the testing data.
For that, we steadily increase the classification threshold and observe the resulting distribution of the detections.
It is observed that the classification accuracy is improved or at least maintained by using our approach for missing data imputation.
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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