Table 4 Strengths and weaknesses of the approaches
| Strength | Weakness | |
|---|---|---|
| Association |
• Fast in implementation • No need to pre-determine parameters •Results can be easily validated by hand calculation |
• Unsatisfactory in predictive power •Does not consider the structures all relevant protein-module associations as a whole •Do not have control for possible over-prediction of associations between frequently occurring domain-disease pairs |
| MLE |
• Good in predictive power •Fast in implementation •Take into account the structures all relevant protein-module associations as a whole |
• Need to pre-determine parameters •Do not have control for possible over-prediction of associations between frequently occurring domain-disease pairs |
| DPEA |
• Satisfactory in predictive power •No need to pre-determine parameters •Have control for possible over-prediction of associations between frequently occurring domain-disease pairs | • Slow in implementation when the number of candidate domain-disease associations is large |
| Bayesian |
• Excellent in predictive power •Take into account the structures all relevant protein-module associations as a whole |
• Slow in implementation when the number of candidate domain-disease associations is large •Do not have control for possible over-prediction of associations between frequently occurring domain-disease pairs •Failure when the log-concave conditions of parameters are not satisfied |
| PE |
• Satisfactory in predictive power •Only one pre-determined parameter •Have control for possible over-prediction of associations between frequently occurring domain-disease pairs | • Slow in implementation when the number of candidate domain-disease associations is large |