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This set was partitioned into smaller sets, using the MCL graph clustering algorithm [ 22].
We performed clustering on the pathway linkage network using the MCL graph clustering algorithm with default parameters [ 41].
From a graph of 52,776 proteins with > 50% similar proteins connected by edges, 3,744 HGs were identified (Additional file 2) using the MCL graph clustering algorithm [ 41].
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The network was clustered into groups of genes sharing similar profiles using the MCL algorithm with an empirical MCL inflation value (1.8) and a global graph for each network was created showing the MCL clusters.
Edge weights of the MCL graph were functions of BLAST E-values with a cutoff of 1E−10.
The line graph generation approach [ 9] transforms the network of proteins connected by interactions into a network of connected interactions and then uses the MCL algorithm to cluster the interaction network.
Data were analyzed using the Graph Pad Prism software package (Graph Pad Software, San Diego, CA, USA).
Finally, the clusters are merged by using the algorithm MCL [ 30] on the graph F. MCL is an efficient algorithm for clustering sparse weighted graphs and ideal for our situation.
Statistical analyses were made using the software program Graph pad prism 3.0 (Graph pad software, La Jolla, CA).
Graphs were drawn using the yEd Graph Editor [ 57].
The graph drawing was performed using the Graph Visualization Software (www.graphviz.org).
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