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In order not to decide beforehand on the units to be processed, we decided in favour of a word-based labelling: each word had to be annotated with one emotion label.
A major drawback of these interactive systems is that they still cannot be considered to be practical tools for a massive database annotation: for instance, consider a case in which a reduction of the 50% of the amount of interactions is achieved; if 1000 onsets had to be annotated, the user would have to deal with 500 elements, which still constitutes a significant workload.
In fact, we have had situations, where a predicted promoter has been withdrawn due to the experimental identification of a second promoter in the same region, but had to be annotated again later due to the confirmation by additional experiments.
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There is also training database of about 150,000 images that also have to be annotated.
Every image in the database has to be annotated to a gold standard that the algorithms must meet.
Also, this human effort is also reduced when compared to the case of annotating the signal from scratch without any initial estimation, which is the typical situation when a new dataset has to be annotated.
This process has to be annotated with the identity of the software agent employed, which may be further annotated with e.g. software version, and any parameters (formally defined in an ontology) used in carrying out the calculation.
Once a tag has been assigned to a sequence it then has to be annotated with its gene name and putative function.
For almost all subsequent analysis to be undertaken, these measured peaks have to be annotated (i.e. matched to the particular metabolites that produced them).
We set the support for association rules to six which means that a minimum of six genes have to be annotated with both phenotype_1 and phenotype_2.
In terms of prediction, given a probably inconsistent vector of input probabilities, one has to find the most probable multiple and consistent GO-DAG paths that the protein has to be annotated to.
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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