Supplementary Figure 2: ROC curve for mode of inheritance gene classifier.
From: Estimating the selective effects of heterozygous protein-truncating variants from human exome data
We train a Naïve Bayes classifier to predict the mode of inheritance in a set of solved clinical exome sequencing cases from Baylor College of Medicine (N=283 cases) and UCLA (N=176 cases). Using data from UCLA as the training dataset, we are able to cross-predict the mode of inheritance in separately ascertained Baylor cases with classification accuracy of 88.0%, sensitivity of 86.1%, specificity of 90.2%, and an AUC of 0.931. Genes that were related to diagnosis in both clinics (overlapping genes) were removed from the larger Baylor set.
