A decision tree to improve identification of pathogenic mutations in clinical practice.

ABSTRACT:

Background

A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic.

Methods

In this work, we use variables categorization and selection for building a decision tree model, and later we measure an

SUBMITTER: do Nascimento PM 

PROVIDER: S-EPMC7063785 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature