Skip to main content

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
    Prec Sens Spec Corr Prec Sens Spec Corr
I BOI1 15 (2.2%) 50 25 99 0.34 4 80 47 0.09
  MYO5 35 (5.2%) 57 67 98 0.60 38 53 97 0.41
  RVS167 19 (2.8%) 0 0 99 -0.01 31 68 96 0.43
  SHO1 37 (5.5%) 70 64 98 0.65 64 84 97 0.71
  YFR024 25 (3.7%) 14 14 97 0.11 25 37 94 0.25
  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
  RVS167 44 (6.2%) 53 62 96 0.54 59 77 96 0.65
  YFR024 123 (17.4%) 47 56 87 0.40 56 78 87 0.58
  YSC84 67 (9.5%) 61 55 96 0.54 60 83 94 0.67