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1. Performance Measures on DNA Dataset

1.1. DNA, trained on sigma70 dataset

 

Table 1: Different classifiers achieved during cross-fold validation on sigma70 training dataset.

Classifier Accuracy (%) auROC auPR Sn (%) Sp (%) F1-Score MCC
LR 92.88 0.9755 0.9789 93.79 91.90 0.9293 0.8583
SVM 91.50 0.9711 0.9738 92.31 90.69 0.9159 0.8306
KNN 84.63 0.9129 0.8858 83.40 85.96 0.8451 0.6954
Decision Tree 76.04 0.7604 0.6990 75.98 76.11 0.7601 0.5220
Naive Bayes 84.40 0.9061 0.8703 85.83 83.00 0.8420 0.6896
Bagging 82.11 0.8936 0.8641 80.16 84.08 0.8174 0.6439
Random Forest 82.16 0.8972 0.8741 78.95 85.43 0.8156 0.6466
AdaBoost 82.72 0.9023 0.8939 82.05 83.40 0.8264 0.6553
Gradient Boosting 85.15 0.9281 0.9293 86.64 83.67 0.8544 0.7050
LDA 90.28 0.9659 0.9702 91.63 88.93 0.9042 0.8067

Note: The number of promoter (+ve) sequences and the number of non-promoter (-ve) sequences are 741 and 1400 respectively.

  ROC

Figure 1: Receiver Operation Characteristic (ROC) curve for different classifiers achieved during cross-fold validation on sigma70 training dataset.

We can download sigma70 dataset from the given link.

 

2. Performance Measures on RNA Dataset

2.1. RNA, trained on PAI244 dataset

 

Table 2: Different classifiers achieved during cross-fold validation on PAI244 training dataset.

Classifier Accuracy (%) auROC auPR Sn (%) Sp (%) F1-Score MCC
LR 88.50 0.9569 0.9607 88.80 88.24 0.8886 0.7770
KNN 81.15 0.8903 0.8759 78.99 83.20 0.8134 0.6293
Decision Tree 78.67 0.7854 0.7348 73.11 84.00 0.7967 0.5938
Naive Bayes 77.89 0.8672 0.8536 89.92 66.40 0.7426 0.5861
Bagging 81.15 0.8802 0.8605 77.31 84.80 0.8233 0.6294
Random Forest 79.10 0.8736 0.8649 78.15 80.00 0.7959 0.5876
AdaBoost 79.51 0.8773 0.8947 76.47 82.40 0.8108 0.5961
Gradient Boosting 81.97 0.8907 0.8968 77.31 86.40 0.8289 0.6493
SVM 83.20 0.9439 0.9549 78.15 88.00 0.8433 0.6703
LDA 59.02 0.5865 0.5849 56.30 61.60 0.6063 0.1896

Note: The number of RNA (+ve) sequences and the number of RNA (-ve) sequences are 125 and 119 respectively for the PAI244 dataset.

  ROC

Figure 2: Receiver Operation Characteristic (ROC) curve for different classifiers achieved during cross-fold validation on PAI244 training dataset.

 

2.2. RNA, tested on PAI300 (There are 300 positive instances, but there are no negative instances) dataset

 

Table 3: Different classifiers achieved during cross-fold validation on PAI300 (300 positive instances) testing dataset.

Classifier Sn (%)
LR 84.67
KNN 72.33
Decision Tree 65.67
Naive Bayes 80.33
Bagging 69.33
Random Forest 71.67
AdaBoost 72.00
Gradient Boosting 71.33
SVM 81.33
LDA 51.67

LR is able to classify 254 instance of 300.

We can download RNA dataset from the given link.

 

3. Performance Measures on Protein Datasets

3.1. DNA-binding Protein, trained on PDB1075 dataset

 

Table 4: Different classifiers achieved during cross-fold validation on PDB1075 training dataset.

Classifier Accuracy (%) auROC auPR Sn (%) Sp (%) F1-Score MCC
LR 90.88 0.9662 0.9717 91.36 90.42 0.9089 0.8186
SVM 89.67 0.9665 0.9714 90.69 88.80 0.8983 0.7967
KNN 84.19 0.9042 0.8778 84.48 83.94 0.8425 0.6851
Decision Tree 75.07 0.7509 0.6883 76.11 74.09 0.7527 0.5039
Naive Bayes 83.72 0.9031 0.8691 83.13 84.35 0.8362 0.6756
Bagging 82.70 0.8941 0.8755 80.70 84.75 0.8233 0.6569
Random Forest 80.65 0.8871 0.8619 76.92 84.35 0.7988 0.6160
AdaBoost 82.88 0.9031 0.9006 83.13 82.59 0.8291 0.6578
Gradient Boosting 85.30 0.9309 0.9338 86.77 83.94 0.8555 0.7096
LDA 87.63 0.9503 0.9563 88.26 87.04 0.8774 0.7541

Note: The number of DNA-binding protein (+ve) sequences and the number of non-DNA-binding protein (-ve) sequences are 525 and 550 respectively for the PDB1075 dataset.

  ROC

Figure 3: Receiver Operation Characteristic (ROC) curve for different classifiers achieved during cross-fold validation on PDB1075 training dataset.

 

3.2. DNA-binding Protein, tested on PDB186 dataset

 

Table 5: Different classifiers achieved on independent PDB186 testing dataset.

Classifier Accuracy (%) auROC auPR Sn (%) Sp (%) F1-Score MCC
LR 83.33 0.8780 0.8357 71.93 89.36 0.7480 0.6259
SVM 81.18 0.9021 0.7921 70.58 86.57 0.7205 0.5783
KNN 77.42 0.8270 0.6715 68.29 81.79 0.6734 0.4990
Decision Tree 70.43 0.6722 0.4768 56.95 77.50 0.5706 0.3457
Naive Bayes 74.73 0.8208 0.6438 73.95 76.29 0.6769 0.4874
Bagging 76.34 0.8206 0.6837 53.04 88.21 0.6052 0.4480
Random Forest 75.27 0.8120 0.6699 49.80 88.36 0.5788 0.4211
AdaBoost 76.34 0.8294 0.7134 63.43 83.14 0.6496 0.4742
Gradient Boosting 78.49 0.8661 0.7706 67.21 86.71 0.6987 0.5519
LDA 79.57 0.8762 0.7817 71.93 86.93 0.7312 0.5937

Note: The number of DNA-binding protein (+ve) sequences and the number of non-DNA-binding protein (-ve) sequences are 93 and 93 respectively for the PDB186 dataset.

  ROC

Figure 4: Receiver Operation Characteristic (ROC) curve for different classifiers achieved during cross-fold validation on PDB186 testing dataset.

We can download DNA-binding protein datasets (PDB1075 and PDB186) from the given link.