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The algorithm adds a node to the model every time that an input column is found to be significantly correlated with the predictable column.
As training steps which have been shown in Fig. 5, the input layer takes an input column data contains six parameters and passes it to the hidden layer.
Then, the hidden layer maps the input column data to the transfer function witch so-called log-sigmoid and given by Eq. (6): Open image in new window Fig. 5 Pseudo-code for LMBP algorithm.
The input column can contain any identifier for the variant and need not be unique.
The input importance shows the relative importance of each input column.
The codon, intron, and untranslated region states use two substitution rates, one rate for when the input column is conserved (no mutations observed) and a second rate when the column is not conserved.
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Specifically, the algorithm identifies the input columns that are correlated with the predictable column.
For discrete attributes, the algorithm makes predictions based on the relationships between input columns in a dataset.
The predictable column in our system is the sequence's number of strategies and input columns are request's characteristics such as Deadline, Budget, FileSize, and Length.
The retinal position of inputs depends on the position of the neuron on the cortical surface relative to the position of thalamocortical input columns.
The first four output columns will have the same format as the first two input columns; PTM site information of species 1 (first two columns) followed by the PTM information of species 2 (last two columns).
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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