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Although carbon source utilization has become a standard method to assess the validity of computational metabolic model predictions, this study was the first to examine this procedure under anaerobic conditions.
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Second, based on comprehensible prediction, this study attempts to identify significant factors that affect analgesic requirement.
Because decision tree-based learning performed the best in analgesic consumption prediction, this study only focuses on the analysis of decision tree-based learning in PCA readjustment prediction.
Unlike analgesic consumption prediction, this study identifies a wider variety of informative attributes for PCA control adjustment prediction, but ANOVA analysis and the chi-square test showed that some of the informative attributes were not significant (p> 0.05).
In agreement with the prediction, this study demonstrates that the high incidence of unmated females in structured populations (~23%% in this study) can be explained by the behavioural and developmental plasticity in C. dactyliperda.
In an attempt to increase the accuracy of CNV prediction, this study employed three algorithms (Birdsuite (Birdseye and Canary), PennCNV, and QuantiSNP) with stringent overlapping criteria to call CNVs on a genome-wide level.
While both models showed good predictions in this study.
The theoretical predictions in this study are in good agreement with those in the literature.
However, future user testing will generate empirical evidence that should help refine the theoretical predictions of this study.
The predictions in this study can be used to inform alloy design of precipitation strengthened magnesium alloys.
While both models showed good predictions in this study, the ANN model was more precise compared to the RSM model.
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