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

BMC Medical Informatics and Decision Making
Priscilla Machado do NascimentoJorge Estefano Santana de Souza

Abstract

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. In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machine-learning (ML) algorithms. The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for False Neutrals, 9% for False Pathogenic, and 92% for True Pathogenic. The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an...Continue Reading

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Citations

Aug 4, 2021·Scientific Reports·Lin WangHong Zhu

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Software Mentioned

COSMIC
MetaSVM
Polyphen
[UNK]
snpSift
XGBoost
SIFT
Clinvar
QueryOR
SPP

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