Research Article
PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins
Table 5
Accuracy of PSO-LocBact compared to other state-of-the-art methods on the well-known benchmark dataset S taken from [
7,
9,
30].
| Gram-negative bacterial proteins | Benchmark dataset S: predictor | Inner membrane (557 proteins) | Outer membrane (124 proteins) | Cytoplasm (410 proteins) | Extracellular region (133 proteins) | Periplasm (180 proteins) | Overall (1,404 proteins) |
| PSO-LocBact | 547 | 116 | 387 | 129 | 171 | 1,350 (96.15%) | Gram-LocEN [25] | 551 | 116 | 374 | 130 | 169 | 1,340 (95.44%) | PSORTb 3.0 [10] | 529 | 114 | 380 | 117 | 168 | 1,308 (93.16%) | CELLO2GO [4] | 519 | 107 | 383 | 128 | 170 | 1,307 (93.09%) | Gneg-PLoc [26] | 454 | 68 | 362 | 59 | 87 | 1,030 (73.36%) | Gneg-mPLoc [7] | 525 | 105 | 357 | 79 | 154 | 1,220 (86.89%) | iLoc-Gneg [24] | 539 | 103 | 367 | 115 | 161 | 1,285 (91.52%) | Fuel-mLoc [23] | 541 | 111 | 379 | 129 | 161 | 1,321 (94.09%) |
| Gram-positive bacterial proteins | Benchmark dataset S: predictor | Cell membrane (174 proteins) | Cell wall (18 proteins) | Cytoplasm (208 proteins) | Extracellular region (123 proteins) | Overall (523 proteins) | |
| PSO-LocBact | 174 | 18 | 206 | 122 | 520 (99.42%) | | Gram-LocEN [25] | 173 | 17 | 203 | 120 | 513 (98.08%) | | PSORTb 3.0 [10] | 169 | 14 | 203 | 112 | 498 (95.22%) | | CELLO2GO [4] | 149 | 10 | 197 | 121 | 477 (91.2%) | | iLoc-Gpos [27] | 167 | 12 | 198 | 110 | 487 (93.12%) | | Fuel-mLoc [23] | 170 | 17 | 202 | 117 | 506 (96.75%) | | Gpos-PLoc [30] | — | — | — | — | 379 (72.47%) | | Gpos-mPLoc [9] | — | — | — | — | 430 (82.22%) | | ML-KNN [28] | — | — | — | — | 78.71% | | wML-KNN [29] | — | — | — | — | 91.49% | |
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