Table 1.
Features | Matthews’ correlation coefficient (MCC) | Accuracy | Sensitivity | Specificity | AUC | P-value |
---|---|---|---|---|---|---|
Amino acid composition (AAC) | 0.394 ± 0.022 | 0.697 ± 0.011 | 0.703 ± 0.013 | 0.691 ± 0.016 | 0.769 ± 0.009 | 0.288 |
Dipeptide composition (DPC) | 0.420 ± 0.023 | 0.709 ± 0.012 | 0.746 ± 0.017 | 0.673 ± 0.014 | 0.780 ± 0.011 | 0.651 |
Composition–transition–distribution (CTD) | 0.330 ± 0.032 | 0.665 ± 0.016 | 0.662 ± 0.033 | 0.668 ± 0.034 | 0.729 ± 0.019 | 0.001 |
Physicochemical properties (PCP) | 0.320 ± 0.017 | 0.659 ± 0.008 | 0.696 ± 0.020 | 0.622 ± 0.017 | 0.725 ± 0.013 | 0.0006 |
Amino acid index (AAI) | 0.397 ± 0.018 | 0.698 ± 0.009 | 0.698 ± 0.026 | 0.699 ± 0.019 | 0.772 ± 0.010 | 0.370 |
Hybrid | 0.381 ± 0.022 | 0.690 ± 0.011 | 0.721 ± 0.036 | 0.658 ± 0.033 | 0.762 ± 0.014 | 0.149 |
PIP-EL | 0.435 ± 0.019 | 0.717 ± 0.010 | 0.707 ± 0.010 | 0.727 ± 0.015 | 0.788 ± 0.011 | – |
The first column corresponds to the performance of individual feature group, hybrid feature, and ensemble learning. The column 2–6 respectively represent the MCC, accuracy, sensitivity, specificity, and AUC value, where each value shown as the average ± SD of 10 alternative balanced datasets. The last column represents a pairwise comparison of AUC between PIP-EL and the other methods using a two-tailed t-test. P ≤ 0.05 indicates a statistically meaningful difference between PIP-EL and the selected composition (shown in bold).