RESULTS
In order to achieve the goal to prioritize VUS with a higher probability of being pathogenic and overcome the limitations of current predictor tools, we developed three models comparing three machine learning approaches (Random Forest, Support Vector Machine and a Neural Network with a Five-layer multilayer perceptron architecture). Models were trained with a set of 82,426 high quality variants from the ClinVar database and tested with a set of variants that had been classified as VUS anytime during the last three years, but had been reclassified with high confidence in any of the 4 informative categories (Pathogenic, Likely pathogenic, Likely Benign, Benign). To increase the size of the training set and ease the interpretation of results we merged the Pathogenic and Likely pathogenic categories into a unique Pathogenic category, and the Benign and Likely Benign category into a unique Benign category.