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Table 4 Discrimination of channels/pores, electrochemical potential-driven transporters and primary active transporters using different machine learning approaches with amino acid occurrence as features

From: Functional discrimination of membrane proteins using machine learning techniques

Method 5-fold cross-validation
  Sensitivity Precision F-Measure Accuracy
  F1 F2 F3 F1 F2 F3 F1 F2 F3 (%)
Bayesnet 0.329 0.735 0.567 0.554 0.515 0.572 0.413 0.606 0.569 54.6
Naive Bayes 0.202 0.757 0.575 0.477 0.512 0.534 0.284 0.611 0.554 51.8
Logistic function 0.533 0.713 0.705 0.689 0.717 0.604 0.601 0.715 0.651 65.
RBF network 0.247 0.727 0.633 0.486 0.593 0.530 0.328 0.654 0.577 54.6
Support vector machine 0.163 0.727 0.826 0.847 0.705 0.529 0.273 0.716 0.645 60.0
k-nearest neighbor 0.629 0.705 0.640 0.634 0.683 0.651 0.632 0.694 0.646 65.6
Bagging meta learning 0.553 0.685 0.737 0.676 0.733 0.625 0.608 0.709 0.676 66.7
Classification via Regression 0.465 0.721 0.721 0.686 0.702 0.602 0.547 0.711 0.656 64.5
Decision tree J4.8 0.543 0.625 0.555 0.526 0.592 0.593 0.534 0.609 0.574 57.2
NBTree 0.471 0.570 0.659 0.553 0.656 0.548 0.508 0.610 0.598 57.7
Partial decision tree 0.520 0.647 0.623 0.551 0.645 0.600 0.535 0.646 0.612 60.0
Neural network 0.549 0.701 0.761 0.695 0.780 0.622 0.613 0.739 0.684 68.1
   Jack-knife test 0.500 0.703 0.729 0.639 0.749 0.607 0.561 0.726 0.663 65.4
   Equal data 0.723 0.743 0.574 0.691 0.712 0.630 0.707 0.727 0.601 68.0
  1. F1: channels/pores; F2: electrochemical potential-driven transporters; F3: primary active transporters. Equal data: Results obtained with a dataset of 502 proteins each in all the three classes of transporters.