Research Article
CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree
Table 4
The performances of different methods in length 17.
| Method | Sn (%) | Sp (%) | F1 | MCC |
| DNABIND [34] | 69.45 | 68.59 | 0.6915 | 0.3804 | DNAbinder [34] | 69.23 | 73.05 | 0.7058 | 0.4231 | DBD-Threader [35] | 56.88 | 92.74 | 0.6931 | 0.5316 | DNA-Prot [35] | 66.65 | 78.75 | 0.7094 | 0.4574 | iDNA-Prot [36] | 75.46 | 74.53 | 0.7511 | 0.5000 | DBPPred [37] | 78.66 | 73.34 | 0.7662 | 0.5208 | PLMLA [38] | 65.62 | 69.51 | 0.6692 | 0.3516 | Phosida [39] | 78.42 | 84.77 | 0.8099 | 0.6331 | LysAcet [40] | 77.17 | 73.76 | 0.7587 | 0.5095 | EnsemblePail [41] | 76.22 | 70.21 | 0.7400 | 0.4652 | PSKAcePred [42] | 70.87 | 67.44 | 0.6968 | 0.3833 | BRABSB [43] | 80.03 | 71.94 | 0.7692 | 0.5214 | SSPKA [44] | 75.49 | 78.09 | 0.7649 | 0.5360 | Proposed algorithm | 80.09 | 78.86 | 0.7960 | 0.5896 |
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