Research Article
Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features
Table 5
The comparison of the predictive results between this paper and existing methods.
| Method | HSP families | HSP20 | HSP40 | HSP60 | HSP70 | HSP90 | HSP100 |
| iHSP-PseRAAACa | Sn (%) | 87.68 | 95.31 | 66.87 | 79.15 | 51.72 | 69.41 | Sp (%) | 96.36 | 84.87 | 98.93 | 86.54 | 99.89 | 99.84 | MCC | 0.82 | 0.99 | 0.69 | 0.54 | 0.3 | 0.83 | Acc (%) | — | — | — | — | — | — | PredHSPb | Sn (%) | 92.16 | 96.09 | 79.75 | 91.17 | 72.41 | 82.35 | Sp (%) | 97.16 | 86.26 | 97.24 | 91.97 | 99.12 | 98.08 | MCC | 0.87 | 0.83 | 0.72 | 0.71 | 0.7 | 0.71 | Acc (%) | 96.36 | 91.91 | 95.96 | 91.87 | 98.43 | 97.48 | ir-HSPc | Sn (%) | 94.63 | 97.45 | 67.92 | 88.49 | 75 | 88.89 | Sp (%) | 96.61 | 95.13 | 98.86 | 98.84 | 99.76 | 99.57 | MCC | 0.8718 | 0.9276 | 0.7307 | 0.8871 | 0.8112 | 0.8846 | Acc (%) | 96.28 | 96.47 | 96.61 | 97.52 | 99.17 | 99.17 | Our predictive model | Sn (%) | 100 | 98.33 | 100 | 100 | 100 | 100 | Sp (%) | 99.92 | 100 | 99.92 | 99.82 | 100 | 100 | MCC | 1 | 0.99 | 1 | 0.99 | 1 | 1 | Acc (%) | 99.93 | 99.72 | 99.93 | 99.85 | 100 | 100 |
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aFeng et al. [ 21]. bKumar et al. [ 25]. cMeher et al. [ 26]. |