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

Nascimento, Priscilla Machado do; Medeiros, Inácio Gomes; Falcão, Raul Maia; Ferreira, Beatriz Stransky; Souza, Jorge Estefano Santana de

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

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dc.contributor.author	Nascimento, Priscilla Machado do
dc.contributor.author	Medeiros, Inácio Gomes
dc.contributor.author	Falcão, Raul Maia
dc.contributor.author	Ferreira, Beatriz Stransky
dc.contributor.author	Souza, Jorge Estefano Santana de
dc.date.accessioned	2020-07-13T15:31:00Z
dc.date.available	2020-07-13T15:31:00Z
dc.date.issued	2020-03-10
dc.description.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	pt_BR
dc.identifier.citation	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	pt_BR
dc.identifier.doi	10.1186/s12911-020-1060-0
dc.identifier.issn	1678-765X
dc.identifier.uri	https://repositorio.ufrn.br/jspui/handle/123456789/29574
dc.language	en	pt_BR
dc.publisher	BMC	pt_BR
dc.rights	Attribution 3.0 Brazil	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/br/	*
dc.subject	Decision tree	pt_BR
dc.subject	VOUS	pt_BR
dc.subject	Pathogenicity	pt_BR
dc.subject	Mutation	pt_BR
dc.subject	Predictor	pt_BR
dc.subject	Precision medicine	pt_BR
dc.title	A decision tree to improve identification of pathogenic mutations in clinical practice	pt_BR
dc.type	article	pt_BR