METHODS OF PREDICTING PATHOGENICITY OF GENETIC SEQUENCE VARIANTS

Recent developments in cost-effective DNA sequencing allows for individualized genomic screening of a subject for genetic sequence variants. Training a pathogenicity prediction model using semi-supervised training methods produces a better model for predicting the pathogenicity of a test genetic sequence variant. Provided herein are methods for predicting the pathogenicity of a test genetic sequence variant by utilizing a training data set comprising labeled benign genetic sequence variants unlabeled genetic sequence variants, the unlabeled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic genetic sequence variants. The genetic sequences are annotated with one or more features and a machine learning model is trained in a semi-supervised process based on the training data. The test genetic sequence is then annotated using the one or more features and the probability that the test genetic sequence variant is pathogenic is predicted based onthe trained machine lear