Figure 4.

Performance of random forest classifier on a test set of 72 cystic fibrosis mutations. (A) Features used for discriminating between disease-associated and neutral nsSNPs. (B) A receiver-operator curve (ROC) showing the true-positive and false-positive rates for the clinical, experimental, and ABC transporter-trained random forests on the cystic fibrosis test set at left. The clinically trained random forest performs best on both the cystic fibrosis test set and the ABC transporter test set.