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With the development of high-throughput methods for identifying protein protein interactions, large scale interaction networks are available.
The second approach is a continuous valve-like approach that allows investigation of averaged effects and large scale interaction and feedback effects.
However, the high cost of experimental methods prevents large scale interaction network mapping of newly sequenced genomes.
We expect that this lack of large scale interaction data will be overcome once more datasets are made available.
The primary goal of present article is to provide tools for large scale interaction calculation for several of the most utilized methods for detection of interactions.
Using the term in the broadest sense, they can be a picture that accompanies a review article, wall charts distributed by journals and companies, small schematic diagrams used to support mathematical modeling efforts or network graphs reflecting known protein interactions based on the results of large scale interaction studies or automated literature mining.
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When large scale, interaction-rich "prior knowledge networks" (PKNs) are converted into models and compared directly to functional data, predictive models specific to individual cell types or disease states can be constructed [ 9, 14] in which the number of edges is significantly lower than in the starting PKN.
Figure 3 shows functional link matrices for several large-scale interaction datasets, including the Treponema network.
Large-scale interaction studies cover functional complexes only to a limited extend.
However, no such resource has been described in a fish species, nor were any large-scale interaction datasets available.
Our method can be applied for protein function prediction in species for which large-scale interaction datasets are available.
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