Fig. 3

Performance of random forest models for splice-disrupting variants on internal cross-validation. Four types of models each were designed using either class weights or SMOTE to address class imbalance; internal performance was assessed using five-fold cross-validation to compare area under the curves (AUC) for each model. Weighted model performance: a SpliceAI only AUC, b DNA variant features only AUC, c DNA variant features + myocardial RNA gene expression AUC, d SpliceAI + DNA variant features + myocardial RNA gene expression AUC. e Gini coefficient showing the importance of a specific feature to the nodes and leaves of the random forest model 4. f The odds ratio for selecting variants confirmed to affect splicing was highest for model 4. SMOTE model performance: g SMOTE SpliceAI only AUC, h DNA variant features only AUC, i DNA variant features + myocardial RNA gene expression AUC, j SpliceAI + DNA variant features + myocardial RNA gene expression AUC. k Gini coefficient showing the importance of a specific feature to the nodes and leaves of the random forest model 4. l The odds ratio for selecting variants confirmed to affect splicing was highest for model 4