Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

ABSTRACT: The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathoge

SUBMITTER: Favalli V 

PROVIDER: S-EPMC8059374 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature