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Table 3 Discrimination of channels/pores, electrochemical potential-driven transporters and primary active transporters using different machine learning approaches with amino acid composition 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 0.582 0.777 0.538 0.606 0.643 0.612 0.594 0.703 0.573 62.1
Naive Bayes 0.496 0.823 0.534 0.626 0.597 0.606 0.554 0.692 0.568 60.7
Logistic function 0.535 0.695 0.619 0.615 0.638 0.601 0.572 0.665 0.610 61.6
RBF network 0.543 0.735 0.625 0.640 0.666 0.603 0.587 0.699 0.614 63.3
Support vector machine 0.469 0.757 0.642 0.675 0.620 0.603 0.553 0.682 0.622 62.4
k-nearest neighbor 0.525 0.707 0.572 0.586 0.588 0.615 0.554 0.642 0.593 59.8
Bagging meta learning 0.541 0.679 0.677 0.646 0.660 0.618 0.589 0.669 0.646 63.6
Classification via Regression 0.492 0.695 0.630 0.599 0.628 0.599 0.540 0.660 0.614 60.8
Decision tree J4.8 0.506 0.580 0.572 0.529 0.581 0.554 0.517 0.580 0.563 55.5
NBTree 0.512 0.669 0.569 0.569 0.610 0.568 0.539 0.638 0.569 68.2
Partial decision tree 0.473 0.649 0.550 0.544 0.551 0.568 0.506 0.596 0.559 55.6
Neural network 0.549 0.717 0.642 0.636 0.659 0.619 0.589 0.687 0.630 63.6
   Jack-knife test 0.571 0.709 0.676 0.664 0.660 0.644 0.571 0.709 0.676 65.4
   Equal data 0.635 0.713 0.624 0.689 0.698 0.591 0.661 0.705 0.607 65.7
  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.