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Table 1 Degree of all proteins, drug targets only and non-drug targets only for the full PIN and various subsets

From: Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

Protein interaction network Nodes examined Mean degree Median degree Degree standard deviation Degree skewness Degree kurtosis Max degree
full PIN -spoke all 14.2 4 28.9 6.6 86.1 789
  drug targets in full PIN 22.5 8 44.8 6.8 84.2 789
  non-drug targets in full PIN 13.5 4 27.1 6.0 66.5 615
drug target subnetwork -spoke   1.7 1 44.7 3.2 15.8 23
non-drug target subnetwork -spoke   12.7 4 26.3 6.8 90.3 709
BioGRID only –spoke all 7.5 3 13.5 7.9 133.0 395
  drug targets in BioGRID only 9.0 3 18.6 5.5 41.3 203
Rual + Stelzl papers only -spoke all 4.3 2 8.8 7.5 81.6 158
  drug targets in Rual + Stelzl only 3.7 2 5.5 6.0 54.7 60
Rual paper only –spoke all 3.8 2 8.4 9.4 127.5 158
  drug targets in Rual only 2.2 1 2.5 3.5 19.4 15
  1. Statistical descriptors of the degree distribution of 11 different PINs whose protein complexes have all been represented as spoke models (i.e. any N-ary data is included by a spoke-model representation). Drug targets have a higher degree on average, even though the standard deviations are equally higher. Degree distribution of drug targets are also more skewed and peaked than non-drug targets. This is different in distributions like the BioGRID database or the Rual and Stelzl papers, where the numerical values are not only significantly smaller but the conclusions might be even the contrary, such as drug targets having a lower degree for Rual and Stelzl. The values of the drug target subnetwork show that interactions between drug targets are scarce and, therefore, the average higher degree of drug targets represent interactions between drug targets and non-drug targets. BioGRID was used by [2], Rual+Stelzl was used by [1] and Rual-only was used by [18].