Table 2 Domain-specific neural network and PSSM results. The application of a domain-specific strategy in the detection of binders reveals the strong effect of the data unbalancing. Class I binding domains have a lower percentage of binders within the datasets and in the corresponding results both PSSM and neural networks display low performances, with no clear benefit in preferring one method to the other. The results of class II binding domains, where a higher percentage of binders (Rvs167, Yfr024, Ysc84) is present, clearly show the prevalence of neural networks. For Boi1 and Boi2 the estimation of PSSM and NN is less significant due to the scarcity of binders.
From: A neural strategy for the inference of SH3 domain-peptide interaction specificity
Class | Domain | Number of Binders | PSSM | NN | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Â | Â | Â | Prec | Sens | Spec | Corr | Prec | Sens | Spec | Corr |
I | BOI1 | 15 (2.2%) | 50 | 25 | 99 | 0.34 | 4 | 80 | 47 | 0.09 |
| Â | MYO5 | 35 (5.2%) | 57 | 67 | 98 | 0.60 | 38 | 53 | 97 | 0.41 |
| Â | RVS167 | 19 (2.8%) | 0 | 0 | 99 | -0.01 | 31 | 68 | 96 | 0.43 |
| Â | SHO1 | 37 (5.5%) | 70 | 64 | 98 | 0.65 | 64 | 84 | 97 | 0.71 |
| Â | YFR024 | 25 (3.7%) | 14 | 14 | 97 | 0.11 | 25 | 37 | 94 | 0.25 |
| Â | YSC84 | 12 (1.8%) | 100 | 33 | 100 | 0.57 | 10 | 80 | 81 | 0.24 |
II | BOI1 | 16 (2.3%) | 17 | 50 | 95 | 0.27 | 19 | 38 | 97 | 0.25 |
| Â | RVS167 | 44 (6.2%) | 53 | 62 | 96 | 0.54 | 59 | 77 | 96 | 0.65 |
| Â | YFR024 | 123 (17.4%) | 47 | 56 | 87 | 0.40 | 56 | 78 | 87 | 0.58 |
| Â | YSC84 | 67 (9.5%) | 61 | 55 | 96 | 0.54 | 60 | 83 | 94 | 0.67 |