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Table 3 Predictive performance of classifying electron transport proteins using different neural networks

From: ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins

  CV Independent
  Sen Spe Acc MCC Sen Spe Acc MCC
kNN 37.7(−) 98.9(+) 85.2(−) 0.53(−) 32.7(−) 96.5(+) 82.1(−) 0.41(−)
RF 64.8(−) 97.1(+) 89.8(−) 0.69(−) 56.3(−) 96.4(+) 87.3(−) 0.61(−)
SVM 74(−) 96.2(+) 91.2(−) 0.74(−) 74(−) 91.7(−) 87.7(−) 0.65(−)
CNN 73.8(−) 95(−) 90.3(−) 0.71(−) 78.2(+) 92.5(−) 89.5(−) 0.69(−)
GRU 83.7 96.3 93.5 0.81 79.8 95.9 92.3 0.77
  1. Note: (kNN: k = 10, RF: n_estimators = 500, SVM: c = 32, g = 0.125, CNN: 128 filters, GRU: 32 filters, (+) for significantly better than GRU, (−) for significantly worse than GRU in a two-proportion z-test)