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Even though the theoretical potential to improve the species predictions is clearly shown above, it could not be realized in the practical predictions combining the predicted dominant species to the models formulated.
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These predictions were clearly correlated to field measured AGB changes with r = 0.69 (Figure 2).
Such predictions are clearly supported by the nature of the geographic variation in the trade-off function between female fecundity or male call duration and flight capability (measured as DLM mass) for the sand cricket Gryllus firmus [33], [34].
In contrast, the PKQuest predictions are clearly incorrect for methoxyflurane.
These predictions were clearly correlated to field measured AGB changes with r = 0.69.
At higher coverage, we found that ASPic CDS predictions are clearly more accurate.
As summarized in Figure 2A and B, the performances of the predictions were clearly different among the ligand types.
As such, these results indicate that our predictions are clearly different from those produced randomly, not only at the phylogenetic level (as shown by orthologous group enrichment) but also at the functional level of proteins participating in specific biological processes.
The 20percentt "apple" and 80percentt "orange" prediction is clearly wrong, but since this is supervised learning with labeled training data, the network is able to figure out where and how that error occurred through a system of checks and balances known as backpropagation.
This prediction is clearly confirmed by the crystal structure.
The prediction is clearly linked to the quality and amount of the data; even with sufficient data, it is not guaranteed that the inferred network corresponds to the appropriate gene network that leads to the observed pattern [ 11, 12].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com