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Table 1 The improvement by incorporating network using CGI_Endeavour and CGI_RRA is significantly correlated with the number of training genes.

From: Network tuned multiple rank aggregation and applications to gene ranking

   CGI_Endeavour CGI_RRA
Expression Network correlation p-value correlation p-value
Compendium BioGRID
DIP
MIPS
0.6614(0.0943)
0.722(0.0796)
0.4212(0.1339)
0.0041(0.0065)
0.0015(0.0040)
0.0668(0.0830)
0.6581(0.0920)
0.7485(0.0855)
0.5232(0.0995)
0.0041(0.0072)
0.0013(0.0046)
0.0217(0.0271)
Stress BioGRID
DIP
MIPS
0.6313(0.0865)
0.6636(0.13300)
0.4620(0.1000)
0.0060(0.0119)
0.0084(0.0212)
0.0387(0.0397)
0.6387(0.0874)
0.7060(0.1333)
0.3961(0.1160)
0.0055(0.0114)
0.0062(0.0162)
0.0725(0.0739)
Cycle BioGRID
DIP
MIPS
0.7776(0.0894)
0.6497(0.1376)
0.5997(0.0823)
0.0007(0.0019)
0.0104(0.0248)
0.0082(0.0131)
0.6585(0.1071)
0.5645(0.1482)
0.5414(0.0890)
0.0063(0.0151)
0.0227(0.0342)
0.0170(0.0233)
  1. The table shows the average Spearman correlation between log-p-fold and the number of training genes together with the standard deviation (3rd and 5th columns) and the mean p-value and its standard deviation (4th and 6th columns) using all combinations of gene expression and interaction data sets.
  2. Elements in the parenthesis are standard deviation.