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A suitable plugin parser for each data instance is chosen in order to parse it.
For each data instance i, (c_{i}) is known precisely according to the experiment design.
After computing the Z-score for each data instance, the algorithm calculates the Z-distribution (i.e., the relative frequency of the raw Z-scores of a population or sample).
For each data instance, the input vector contains 319 feature values, including 220 (20 × 11) PSSMs, 66 (6 × 11) OBVs and 33 (3 × 11) SS elements.
For each data instance (gene) in our dataset, each attribute takes on the value "yes" or "no" to indicate whether or not (respectively) that gene's protein product interacts with the protein represented by that attribute.
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Let ψ contain all positive data instances and 1% randomly selected negative data instances from ϕ. Predict ϕ using the model trained by the data instances in ψ based on the RF and then get F +, where F + is the fraction of the tree votes for the positive class in each data instance.
In particular, since PSSM has 20 scores for each sequence position, it gives rise to 220 inputs for a data instance with eleven residues.
For each sample the full list of stored data instances can be visualised, and for any data instance the set of associated metadata and files.
Because each residue was encoded with 20 PSSM scores and 3 biochemical features, the input vector contained 253 values for a data instance with eleven residues.
Table 1 reports the computational results for three different data instances.
After the model is built, its predictive accuracy is then measured in a separate subset of the data, called the test set, where the algorithm knows only the values of the predictor attributes (and not classes) for data instances.
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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