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 classify clinically reportable variants. Assays were performed between 2020 and 2023 yielding

SUBMITTER: Doig KD 

PROVIDER: S-EPMC12570631 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature