Performance comparison of different formulations of the SVM-MIL algorithm in predicting isoform functions.

<p>A. The histogram shows the score distribution of the instances in the positive bags and the negative bags in the training set. Different threshold choices in mi-SVM are based on the distribution of scores of negative genes. The first threshold is equal to the mode of distribution of scores from negative instances in the training set. The second threshold is equal to the 75% percentile of scores of the negative instances in the training set. The third threshold is equal to the maximum score of negative instances in the training set. B. This panel illustrates how different thresholds and formulations can divide the isoforms in a positive bag into positive, negative and neutral classes. Three thresholds in mi-SVM represent different degrees of strictness for assigning labels. The first threshold is the least strict, which assigns most of the isoforms from positive genes as positive, whereas the third threshold is the strictest, which in general leaves only one positive instance in every positive bag. For the MI-SVM formulation, only one isoform per positive gene is assigned as positive, and other isoforms are dropped (<i>i.e.</i> neutral class). C. Performance comparison of three different threshold choices for the mi-SVM formulation, the MI-SVM formulation and the MI-SVM formulation with random witness selection. This plot shows that the mi-SVM formulation with threshold-2 performs best in terms of AUC.