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degree) ranked No. 9 among U.S. medical schools for primary care, and the College of Human Medicine (MD degree) has regularly ranked among the top forty U.S. medical schools for primary care.
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In the following we consider a model to assess the specificity of functional information in protein interaction networks (and find that node degree ranks genes by their probability of random interaction).
For example, while average "real" prediction performance of GO groups is high when using protein interactions (mean AUC 0.7), the node degree ranking mean AUC is 0.63 – far above the 0.5 expected by chance.
The betweenness ranks correspond quite well to the degree ranks with some exceptions.
The degree ranks for the three networks are almost identical (see Tables 3, 4 and 5).
The degree ranks showed a slightly higher correlation between the RNAseq and Bead GRN of r=0.22 (RNAseq-Bead, p≤2.2 e−16) compared to r=0.16 (Bead-Oligo, p≤2.2 e−16) and r=0.16 (RNAseq-Oligo, p≤2.2 e−16).
We next computed the combined score that we defined earlier as the average of the degree ranks in the transcriptional and protein interaction networks: S = (R T + R P )/2.
Some karate schools for example have teaching ranks such as "juin-shidoin" and "shidoin" in addition to black belt degree ranking.
Over the period, the correlation (Spearman ρ) between country degree rank and country bandwidth rank ranged between.75 and.81 (p<.001 in all cases) indicating that high ranking bandwidth countries also tended to be high ranking degree countries.
To show this, we calculate the Spearman correlation coefficient between the degree ranking of nodes and their propagation rate ranking.
(B) As the network grows the rank-frequency plots of the bipartite degree ranks also more clearly follow the Zipf's law, (P r) sim r^{-b}) with (b=1.13 pm0.03) (solid line).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com