The Benefit of Weighting Functional Labels
Some researchers [3,11,39] suggested that protein function prediction should be addressed as an unbalanced classification algorithm. Additional experiments were conducted to investigate the benefit of using Y˜ (weighted) in place of Y (unweighted). Y˜ differentially weights the labels, and puts more emphasis on the labels that have fewer member proteins. In contrast, Y equally weights all the labels. The definition of Y and Y˜ are provided in the section of Method. We report the results of MNet using Y˜ (weighted) and Y (unweighted) in Table 2 of the Additional File 1.
Table 2  Runtime (in seconds).
Dataset  GO  MNet  SW  ProMK  MSkNN  LIG  OMG
Yeast  BP  2256.26  151.88  72.61  16.60  938.10  65.51
CC  282.10  36.39  31.84  12.47  272.89  15.76
MF  390.10  46.07  36.83  12.42  322.11  18.97
Human  BP  19923.15  120.09  628.30  42.15  10309.56  447.01
CC  1003.46  17.57  350.92  31.69  1496.33  96.61
MF  1633.55  25.42  369.92  32.62  2195.25  116.59 MNet based on Y˜ performs better than MNet based on Y , especially for the BP labels, which are more unbalanced than the CC and the MF labels. MacroF1 is more affected by the labels that contains fewer proteins, and the performance difference between MNet based on Y˜ and MNet based on Y is more obvious for MacroF1 than for the other metrics. This fact shows that MNet based on Y˜ can more accurately predict the labels with few member proteins than MNet based on Y , and explicitly considering the unbalanced problem in data integration based protein function prediction can boost the prediction accuracy. These results support our motivation to use Y˜ instead of Y. However, we point out that there is still room to handle the unbalanced label problem for protein function prediction more efficiently, and how to achieve a more efficient weighting scheme for the labels is an important future direction to pursue.