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The input data comprised a table of connection results with node columns, connected target columns, and their connections.
On the other hand, it performs similar to the original random walk with restarts algorithm (with raw scores) for highly connected target genes.
For yeast, we included an additional enrichment test, which connected target genes not to pathways in which they participate, but to pathways in which the target's known transcription factors participate.
For instance, if a random walker is at a drug node, it can jump to one of the connected target nodes with the probability λ, or jump to connected drug nodes with the probability 1 − λ.
Therefore, Zhao and Gurusamy [20] further investigate the connected target coverage problem.
In this paper, we use distributed learning automata (DLA) to design a new scheduling algorithm for solving the connected target coverage problem in DSNs.
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Saturated neurons, with the allocated number of strongly connected targets, do not form further connections.
Moreover, for connections with enrichment |score|>1.25 (i.e. P<0.002), more than half (28 out of 51, vs. 21.3% by random chance) of connected targets participate in at least one common pathway.
Such maturation-triggered pruning is crucial for preventing all neurons from being recruited into the second group; without it, all connections from the training neurons, however weak initially, are eventually strengthened to maturation due to the consistent activations of the training neurons and their strongly connected targets.
They demonstrated that the attenuation of signalling is significantly enhanced when several upstream processes are inhibited, and that this weakening is most pronounced in signals downstream of serially connected targets.
Genes with high connectivity (query genes) provide alternative routes to connect target nodes (i.e., mutations on these target nodes could be buffered by different queries).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com