Table 4 10-fold cross validation performances on the first group of dataset
From: Deep learning architectures for prediction of nucleosome positioning from sequences data
Method(Species) | Accuracy | Sensitivity | Specificity | |||
|---|---|---|---|---|---|---|
μ | σ | μ | σ | μ | σ | |
iNuc-PseKNC(CE) | 86.90 | x | 90.30 | x | 83.55 | x |
iNuc-PseKNC(DM) | 79.97 | x | 78,31 | x | 81.65 | x |
iNuc-PseKNC(HM) | 86,27 | x | 87,86 | x | 84,70 | x |
DLNN-3(CE) | 89.60 | 0.8 | 93.36 | 1.27 | 85.93 | 2,13 |
DLNN-3(DM) | 85.54 | 1.13 | 87.60 | 2.55 | 83.42 | 2.65 |
DLNN-3(HM) | 84.65 | 2.16 | 89.67 | 2.83 | 79.64 | 4.29 |
DLNN-5(CE) | 89.62 | 2.45 | 93.04 | 3.68 | 86.34 | 5.54 |
DLNN-5(DM) | 85.60 | 0.75 | 87.81 | 2.79 | 83.33 | 2.74 |
LNN-5(HM) | 85.37 | 1.91 | 88.34 | 1,82 | 82.29 | 4.86 |