Table 4

AUC scores (%) of the prediction results of the proposed method with different combinations of training and testing negative samples

Test negatives
TNNCNN-TNNANN-CNNCNNANN-CNN + TNNANN-TNNANN
Train NegativesTNN93.3794.7995.4094.8194.7494.8694.62
CNN-TNN90.6696.1896.0796.1994.5396.2096.05
ANN-CNN91.1996.0096.7995.9595.1896.0095.82
CNN90.6896.1796.0696.1894.5296.2096.04
ANN-CNN + TNN91.7395.8796.5795.8995.1695.9195.71
ANN-TNN90.6796.1496.0896.1694.5596.1996.02
Test negatives
TNNCNN-TNNANN-CNNCNNANN-CNN + TNNANN-TNNANN
Train NegativesTNN93.3794.7995.4094.8194.7494.8694.62
CNN-TNN90.6696.1896.0796.1994.5396.2096.05
ANN-CNN91.1996.0096.7995.9595.1896.0095.82
CNN90.6896.1796.0696.1894.5296.2096.04
ANN-CNN + TNN91.7395.8796.5795.8995.1695.9195.71
ANN-TNN90.6796.1496.0896.1694.5596.1996.02

Underlined numbers represent best-performing scores

Table 4

AUC scores (%) of the prediction results of the proposed method with different combinations of training and testing negative samples

Test negatives
TNNCNN-TNNANN-CNNCNNANN-CNN + TNNANN-TNNANN
Train NegativesTNN93.3794.7995.4094.8194.7494.8694.62
CNN-TNN90.6696.1896.0796.1994.5396.2096.05
ANN-CNN91.1996.0096.7995.9595.1896.0095.82
CNN90.6896.1796.0696.1894.5296.2096.04
ANN-CNN + TNN91.7395.8796.5795.8995.1695.9195.71
ANN-TNN90.6796.1496.0896.1694.5596.1996.02
Test negatives
TNNCNN-TNNANN-CNNCNNANN-CNN + TNNANN-TNNANN
Train NegativesTNN93.3794.7995.4094.8194.7494.8694.62
CNN-TNN90.6696.1896.0796.1994.5396.2096.05
ANN-CNN91.1996.0096.7995.9595.1896.0095.82
CNN90.6896.1796.0696.1894.5296.2096.04
ANN-CNN + TNN91.7395.8796.5795.8995.1695.9195.71
ANN-TNN90.6796.1496.0896.1694.5596.1996.02

Underlined numbers represent best-performing scores

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