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This resulted in a mouse protein interaction network consisting of 20,200 binary protein interactions.

The mouse protein interaction network from IntNetDB [ 22] contains 4,991 proteins connected by 17,489 interactions.

The IntNetDB mouse protein interaction network was partitioned into densely connected subnetworks, or modules, using Markov Clustering algorithm (MCL, [ 23]).

The nature of this diversity was also illustrated using the same workflow implemented on target proteins that are also hubs in the mouse protein interaction network (as depicted in Figure 2(b)).

For any given pair of genes, A and B, their shared interaction partners were calculated as (N(A)∩ N(B)) in relation to the degree of N(A) and N(B) of the mouse protein interaction network overall.

Each of the 7965 target genes from the gene regulatory network modules in OntoGeNet was mapped to its protein product counterparts in the mouse protein interaction network (of which 2133 had at least one interaction).

Similar(54)

However, very few top-700 metagene pairs from any network overlapped with known human or mouse protein interactions.

Finally, we generated a mouse protein-protein interaction network in adipose tissue to probe the biological relationships among genes forming a co-expression network with Nnat.

Because of the absence of the large-scale mouse protein-protein interaction experiment and the function similarity between human-mouse orthologous genes, here we use the protein connectivity of corresponding human orthologs to approximately represent that of the mouse KO genes.

By using BioNetBuilder, a cytoscape plugin package http://err.bio.nyu.edu/cytoscape/bionetbuilder/ with supported databases DIP, BIND, Prolinks, KEGG, HPRD, The BioGrid and GO, we thus generated a mouse protein-protein interaction network which had 11777 unique nodes (genes) and 107813 unique edges (links).

Nevertheless, our results provide evidence for the fact that the use of GO annotations from orthologous mouse proteins or protein interaction partners aids in ranking candidate genes and proteins accurately if the latter do not already possess a suitable GO annotation.

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