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Initially, one of the main objectives of MaGe was to allow experts to make relevant annotation using (i) a gene annotation editor giving access to the results of each executed method and links to common useful resources, and (ii) synteny results and metabolic network predictions, the combination of which is very helpful to make hypotheses about the biological function of un-annotated genes.
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As already demonstrated by Dybowski et al. [ 9] for co-receptor tropism prediction, the combination of structural and sequence-based classifiers can improve prediction performance significantly.
For polytomous prediction, the combination of dichotomous 1-versus-1 models is an interesting alternative to true polytomous (all-at-once) models.
In terms of outcome prediction, the combination was superior to either assay on its own (odds ratios 2.95 and 3.5; P-values 0.13 and 0.08).
For example, Cufflinks appears to yield a set of predictions that is more complementary to GeneMark than GIIRA predictions because the combination of GeneMark and Cufflinks leads to a higher sensitivity and fewer missed exons than GeneMark combined with GIIRA.
Four studies combined SOFA-based derivatives with admission severity of illness scores, and they all reported on improved predictions for the combination.
The data support the prediction that the combination of cocaine and histamine H1 receptor antagonists could have enhanced potential for abuse relative to either drug alone.
Hysteresis energy shows promise in bridging the gaps associated with life prediction when the combination of both creep and fatigue scenarios are present.
The lessons learned from advancing secondary structure prediction through the combination of machine learning and evolutionary information suggest that there is yet no way to tell.
With 90% prediction sensitivity, the combination of three identified N-glycans (m/z 2,472.9, 3,241.9, and 4,052.2) was sufficient for the detection of HCC in a population with high rates of hepatitis C viral infection.
These results along with the ADTree in Figure 1 make it clear that while individual attributes may contribute more or less to a prediction problem, the combination of these features gives us a multi-dimensional view of how the two classes are separated.
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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