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Some models showed high variability which was presumably due to spatial heterogeneity, temporal variability or sampling uncertainty.
The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability.
These models showed high stability to data variation in the leave-one-out cross-validation procedure (q2 = 0.912, scv = 0.289 and q2 = 0.957, scv = 0.211).
Estimated breeding values (EBVs) of the two types of models showed high and positive correlations (0.92 0.99), which meant that these models showed similar utility for predicting breeding value.
The models showed high fidelity in predicting soy carbohydrate hydrolysis over broad ranges of soy flour loading (5 25%) and enzyme activities: per g soy flour, cellulase, 0.04 30 FPU; xylanase, 3.5 618 U; pectinase, 0.03 120 U; and α-galactosidase, 0.01 60 U.
The proposed neural network models showed high accuracy in predicting the P b, B ob, and µ ob values, and achieved the lowest relative error, lowest absolute percent relative error, lowest minimum error, lowest maximum error, and lowest standard deviation of relative error.
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The models showed higher numbers of Lyme disease cases in places where there are fewer foxes.
Feasibility studies of 11C-4DST in rodent tumor models showed higher uptake than that of 18F-FLT and reflect the DNA synthesis rate [12,13].
The single-node/single-link models showed higher von Mises stress in the caudal half of the mandible compared to the other models (Fig. 7).
The fully adjusted models showed higher rates than in Caucasians in all except for South Asians.
Of the gene models that showed an FPKM of >0.05 in at least one sex, 7,977 gene models (37.4%) showed higher expression in males, while 13,375 (59.7%) gene models showed higher expression in females.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com