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Calculating feature descriptors of the sampled points based on their local chemical neighborhood.
Therefore, S=2, i.e., S+1=3 time steps were used for calculating feature vectors to estimate error degrees in these experiments.
As illustrated in Supplementary Figure 1, calculating feature overlaps for large datasets (e.g. millions of sequence alignments) is substantially faster than using the tools available on the public Galaxy web site.
However, the whole data set was used for calculating feature importance (Fig. 1), for the list of biomarkers we found (Table 2) and for the enrichment analyses (Tables 3 and 4), since these parts of the work did not involve training and testing.
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After calculating features for each frame, the GFCC are introduced as the inputs to the binary DNN classifier for each frequency channel.
Even more, noisy values may be obtained when calculating features that cannot be discriminated within the given database or cannot be extracted in good quality from the data due to experimental restrictions.
The method utilizes locally calculated feature vectors (FVs) in point cloud data.
The feature vector is built in two steps: first, it calculates feature vectors for specific pocket atoms (AFVs) which are then aggregated into feature vectors of the inner points (IFVs).
Calculated feature values for each image are indicated.
For random forests, one can instead use the calculated feature importance measures to perform feature selection.
Each step we calculate feature scores for GO terms based on KWs and co-annotation of gene products, build a classifier and make a prediction followed by manual curation.
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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