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Betweenness centrality was computed for all genes in the network.
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The centrality is computed using the user-preferred values for the first filtering constraint and additionally the user can choose the values of (alpha) and (beta).
Degree centrality is computed as the summation of edges incident with a node normalized by the number of nodes within the network[13].
Closeness centrality is computed as the inverse of the sum of geodesic distances from specific farmer i to the other n − 1 farmers.
Not only the clustering results using the infinite betweenness centrality for this kind of networks are better, but also the nodes with biggest influences are more precisely detected when the betweenness centrality is computed over the periodic graph.
Degree, betweenness, average shortest path length (ASPL) and eigenvector centrality were computed based on the largest component of the network using R package Igraph (Table S4).
For each individual the values of B-, C-, and P-centrality were computed at every iteration t.
For each protein, degree was computed as the number of proteins to which it is connected, and betweenness and closeness centralities were computed using the NetworkX package (http://networkx.lanl.gov/).lanl.gov/
For each protein, degree was computed as the total number of interactions in which it is involved, and betweenness and closeness centralities were computed using the NetworkX Python library (https://networkx.github.io/).
Node centrality analysis was computed using the Cytoscape 2.8.2 plugin Centiscape 1.2 [ 63] to identify nodes (proteins) with central positions within the networks.
The area under the curve (AUC) for each ROC was computed for each centrality metric and compared in all subjects using multiple pairwise t-tests with Bonferoni correction to test for differences between the centrality metrics.
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