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The graph was generated with the Gephi (https://gephi.org) network visualization tool using a Fruchterman-Reingold network layout.
The point of the exercise is that the graph was generated entirely by computer; if you fed in the stories from a different period of time, the results would be entirely different.
The graph was generated using GraphPad Prism.
The graph was generated by placing the genes in each set (i.e., positive, negative, and predicted) into 5 equally spaced bins based on significance score.
The graph was generated employing the STRING on-line resource based on high confidence data related with 'co-expression/co-ocurrence'experimental/biochemicalcal data' and 'association in curated database/text mining' [ 17].
This figure compares the substitution rates between 12 coding genes on heavy strand vs 1 on light strand, but information on how the graph was generated for the 12 genes cannot be found.
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The simulations in the section follow the bivariate Pareto model and parameters introduced in Eq. 7. We use the same set of parameters as for Fig. 1, and the graph is generated according to the Metropolis technique in section "Random graph generation".
The graph is generated using the Energy-Water nexus tool (http://nexus.hydroviz.org).hydroviz.org
The graph is generated using the Equation 13 which do not take into account the velocity of vehicle.
Once the graph is generated, the shortest path that minimizes the sum of weights from the starting node to the final state across the wDAG is found using the Dijkstra [33] algorithm.
Results, plotted in the graph, were generated from the average value of three independent experiments.
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