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Exact(2)
Here, again, there are differences between these three datasets.
The features observed in Figs. 8 and 9 suggest that, for the torsion versus PGTA and the torsion versus rocking correlations, the differences between these three datasets can be explained by local soil properties.
Similar(58)
All three of their anisomycin datasets were studied, and the pairwise correlations between the RRT results for these three datasets were very high, ranging from 0.89 to 0.998.
We analyzed for transcripts that were ≥1.5 fold differentially expressed between RH-ERP and GT1 across these three datasets, to identify genes that were consistently up or downregulated between RH-ERP and GT1.
Subsection 3.5 directly estimates the degree of mismatch between these two datasets using information theoretic measures.
Despite the differences between these two datasets, our models trained on HumanEva dataset obtain a relatively close result as method in [43].
In order to evaluate the relationships between soil fertility and remotely sensed data, partial least-squares (PLS) and a multiple linear regression (MLR) analysis between these two datasets were carried out.
As individuals in these two datasets are from the same population, it is expected that no difference exists between these two datasets.
In general, the overlap between these two datasets was small.
There were 9,217 SNP and 44 bulls in common between these two datasets.
Only 8 sRNAs are shared between these two datasets of which four were validated by Northern blotting [ 11].
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