Medicine and Health

Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning

R. T. Mclaughlin, M. Asthana, et al.

This groundbreaking study explores the use of machine learning to improve the accuracy of somatic mutation identification, enhancing tumor mutational burden estimates critical for immunotherapy response. Conducted by R. Tyler McLaughlin and colleagues, the research showcases state-of-the-art performance in separating somatic from germline variants, revolutionizing the field of precision oncology.