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Figure 5B shows the correlation for paths of length four (Pearson r = 0.98, P < 10 12), and similar statistically significant correlations were obtained for paths of length three and five (Supplementary Fig. S2). Figure 5C exemplifies a top-ranking path connecting the viral interacting protein TRAF2 to the differentially expressed gene IRF7.
We combined the 1% top-ranking paths into the network shown in Figure 6.
Top-ranking paths were much more likely to contain infection-related proteins, and this likelihood was highly correlated with path score.
The four human proteins that interact with PB2 through top-ranking paths were all found as likely upstream regulators of interferon expression (Fig. 6, red sub-network).
The proposed traffic engineering framework relies on a non-cooperative potential game, built upon locator and path ranking costs, that indicates efficient equilibrium solution for the edge-to-edge load-balancing coordination problem.
Therefore, by using the pathways identified by the path ranking method, we are upscaling both 3M and HME3M to analysis of global metabolic networks.
To show the scalability of the path ranking procedure to global metabolism in Figure 1, we present running times for analysis of the entire KEGG yeast metabolic network using the Gasch microarray data (Gasch et al., 2000) and a same gene penalty of ρ = 0. Figure 1 shows the effect on the running time of the path ranking algorithm at various control parameter settings and network sizes.
PARIS — As Andreas Seppi continued to climb the ATP Tour men's singles rankings this spring, his path continued to cross with that of Novak Djokovic, the tour's No. 1-ranked player.
It was also shown that relative rankings of central paths and their constituent shorter paths can be used to predict the direction of fitness change in an evolutionary trajectory.
The bias of pathway ranking methods towards shorter path lengths results in the identification of only a minimal subset of the full metabolic response.
The second ranking criterion ranks KEGG pathways based on the change in length and number of paths connecting sources and sinks between pathways that exclude or include the nodes representing the genes in an input list.
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