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Hordijk and Steel [ 23] verified this claim by generating random networks of reversible ligation/cleavage reactions between strings up to length n = 20, where each molecule had the probability P of catalyzing each reaction.
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Finally, for those queries we verified their statistical significance by using all random models and we measured the average time required for generating random networks and searching the queries.
Meanwhile, the generating random network model is a key to identifying motifs.
To overcome this, we generated random networks by combining edge rewiring method and simulated annealing algorithm.
To take into account the super-hubs, here we generate random networks by randomly rewiring the links of metabolic networks, which preserves the same degree distribution [ 1].
By mimicking the degree distribution of nodes within the essential network in the generated random networks, the average number of nodes encompassed within the largest connected component increased from 33% (using a standard randomisation strategy) to 88% over 1000 iterations.
For random network, we generate random networks with nodes in a cube and set Tr to.
In early work, PPI networks were rewired (link pairs were shuffled) to generate random networks [ 41].
First, we generated random networks, or R. For Luscombe network, we permutated TF entries of adjacency lists at random.
Different algorithms are available in the literature to generate random networks with tunable parameters [ 23- 27].
mfinder and fanmod, both generate random networks in their process of motif identification.
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