Figure 1
From: MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
Feature ranking for Disease negative set versus SNP negative set (Iter. 1), shown by means of the average AUC using 10-fold cross-validation. The linear support vector machine (SVM) classifier was trained with only the specific feature (or feature subset) that was being tested. As a control, each training example had a randomly generated numerical value computed. AUC values for all features were then compared with the AUC produced by a classifier trained with only the randomly generated attribute by means of a Bonferroni corrected t-test (P < 0.05). Significantly different AUC values compared to the random attribute are indicated by asterisks in parentheses for the respective data sets (significant Disease negative set feature, significant SNP negative set feature). Features are ranked by reference to the Disease negative set.