Table 5 Comparison of the results reported for the 1189 benchmark (in percentage %)
From: Proposing a highly accurate protein structural class predictor using segmentation-based features
References | Method | All-α | All-β | α/β | α+ β | Overall |
|---|---|---|---|---|---|---|
[8] | Bayes Classifier | 54.8 | 57.1 | 75.2 | 22.2 | 53.8 |
[45] | Logistic Regression | 57.0 | 62.9 | 64.7 | 25.3 | 53.9 |
[56] | FKNN | 48.9 | 59.5 | 81.7 | 26.6 | 56.9 |
[57] | WSVM | - | - | - | - | 59.2 |
[53] | Specific Tri-peptides | - | - | - | - | 59.9 |
[21] | IB1 | 65.3 | 67.7 | 79.9 | 40.7 | 64.7 |
[38] | AAD-CGR | 62.3 | 67.7 | 66.5 | 63.1 | 65.2 |
[58] | SVM | 75.8 | 75.2 | 82.6 | 31.8 | 67.6 |
[54] | AATP | 72.7 | 85.4 | 82.9 | 42.7 | 72.6 |
[16] | AADP-PSSM | 69.1 | 83.7 | 85.6 | 35.7 | 70.7 |
[55] | SCPRED | 89.1 | 86.7 | 89.6 | 53.8 | 80.6 |
[24] | RKS-PPSC | 89.2 | 86.7 | 82.6 | 65.6 | 81.3 |
[27] | MODAS | 92.3 | 87.1 | 87.9 | 65.4 | 83.5 |
[26] | AAC-PSSM-AC | 80.7 | 86.4 | 81.4 | 45.2 | 74.6 |
[22] | Physicochemical-based features | 80.2 | 83.6 | 85.4 | 44.6 | 74.8 |
[5] | Structural-based features | 92.4 | 87.4 | 82.0 | 71.0 | 83.2 |
[6] | Structural-based features | 93.7 | 84.0 | 83.5 | 66.4 | 82.0 |
This Study | PSSM-S | 92.6 | 86.0 | 76.7 | 64.3 | 79.7 |
This Study | SPINE-S | 91.9 | 88.3 | 78.9 | 61.7 | 80.3 |
This Study | PSSM-SPINE-S | 98.2 | 91.5 | 83.8 | 72.2 | 86.3 |