1. Universidade Federal do Rio Grande do Norte
2. Centro de Tecnologia
3. DEB - Departamento de Engenharia Biomédica
4. CT - DEB - Artigos publicados em periódicos

Título:	A decision tree to improve identification of pathogenic mutations in clinical practice
Autor(es):	Nascimento, Priscilla Machado do Medeiros, Inácio Gomes Falcão, Raul Maia Ferreira, Beatriz Stransky Souza, Jorge Estefano Santana de
Palavras-chave:	Decision tree;VOUS;Pathogenicity;Mutation;Predictor;Precision medicine
Data do documento:	10-Mar-2020
Editor:	BMC
Referência:	NASCIMENTO, P. M.; MEDEIROS, I. G.; FALCÃO, R. M.; FERREIRA, B.S; SOUZA, J. E. S.. A decision tree to improve identification of pathogenic mutations in clinical practice. BMC Medical Informatics and Decision Making, v. 20, p. 52, 2020. Disponível em: https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1060-0. Acesso em: 10 jul. 2020. https://doi.org/10.1186/s12911-020-1060-0
Resumo:	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 and compare its accuracy with four known mutation predictors and seventeen supervised machinelearning (ML) algorithms. Results: 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. Conclusions: The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms. Besides, the decision tree algorithm is easier to apply in clinical practice by non-IT experts. From the cancer research community perspective, this approach can be successfully applied as an alternative for the determination of potential pathogenicity of VOUS
URI:	https://repositorio.ufrn.br/jspui/handle/123456789/29574
ISSN:	1678-765X
Aparece nas coleções:	CT - DEB - Artigos publicados em periódicos

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