-specific machine learning model predicts variant pathogenicity with high accuracy.

Identification of novel variants outpaces their clinical annotation which highlights the importance of developing accurate computational methods for risk assessment. Therefore our aim was to develop a -specific machine learning model to predict the pathogenicity of all types of variants and to apply this model and our previous specific model to assess variants of uncertain significance (VUS) among Qatari patients with breast cancer. We developed an XGBoost model that utilizes variant information such as position frequency and consequence as well as prediction scores from numerous in silico tools.