Using artificial intelligence (AI) to model clinical variant reporting for next generation sequencing (NGS) oncology assays

Abstract

Background: Targeted next generation sequencing (NGS) of somatic DNA is now routinely used for diagnostic and predictive reporting in the oncology clinic. The expert genomic analysis required for NGS assays remains a bottleneck to scaling the volume of patients being assessed. This study harnesses data from targeted clinical sequencing to build machine learning models that predict whether patient variants should be reported. Methods: Three somatic assays were used to build machine learning prediction models using the estimators Logistic Regression, Random Forest, XGBoost and Neural Networks. Using manual expert curation to select reportable variants as ground truth, we built models to classi..