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Table 4 The performance comparison of models in protein centric task. The table reports the true positive (TP), false negative (FN), false positive (FP), number of proteins that have at least 1 prediction label (NP), the precision, recall, F measure, and Matthews correlation coefficient (MCC) for different features used in the models. The features used are k-mer, GO terms (BP, CC, and MF), and when both k-mer and GO terms are combined. The number of layers in neural network are three where the dimension of the first two layers are 128 and 64, and the last layer dimension is equal to the number of metabolic pathways

From: Predicting metabolic pathway membership with deep neural networks by integrating sequential and ontology information

Features Method TP FN FP NP Precision Recall F1 measure MCC
k-mer NN 1312 4183 3481 920 0.216 0.178 0.195 0.246
  SVM 206 5289 41 149 0.823 0.074 0.136 0.176
  RF 201 5294 0 71 1.000 0.052 0.099 0.190
  KNN 792 4703 457 332 0.624 0.132 0.217 0.300
BP NN 2773 2722 2256 984 0.646 0.513 0.572 0.521
  SVM 2283 3212 711 888 0.796 0.449 0.574 0.560
  RF 1709 3786 170 623 0.906 0.375 0.531 0.529
  KNN 1391 4104 424 787 0.755 0.374 0.500 0.438
BP+k-mer NN 2760 2735 1830 973 0.648 0.500 0.565 0.544
  SVM 2301 3194 709 883 0.804 0.448 0.575 0.563
  RF 1198 4297 42 371 0.970 0.227 0.368 0.456
  KNN 1430 4065 394 773 0.764 0.375 0.503 0.449
CC NN 1945 3550 3616 926 0.422 0.355 0.386 0.343
  SVM 1117 4378 283 534 0.768 0.255 0.383 0.400
  RF 1009 4486 267 445 0.736 0.215 0.333 0.379
  KNN 966 4529 436 525 0.627 0.204 0.308 0.346
CC+k-mer NN 2178 3317 2523 827 0.493 0.348 0.408 0.421
  SVM 1213 4282 302 551 0.784 0.270 0.401 0.418
  RF 659 4836 25 212 0.977 0.132 0.232 0.338
  KNN 1026 4469 450 535 0.675 0.224 0.336 0.358
MF NN 2429 3066 2950 844 0.545 0.400 0.462 0.439
  SVM 1703 3792 423 646 0.785 0.306 0.441 0.496
  RF 1580 3915 454 604 0.786 0.316 0.451 0.470
  KNN 1313 4182 576 635 0.642 0.262 0.372 0.405
MF+k-mer NN 2520 2975 2900 868 0.580 0.399 0.472 0.454
  SVM 1771 3724 449 665 0.783 0.326 0.460 0.504
  RF 985 4510 18 275 0.968 0.157 0.270 0.417
  KNN 1427 4068 533 612 0.697 0.272 0.391 0.432