Souza, Jorge Estefano Santana deGomes, Daniel Henrique Ferreira2022-04-052022-04-052022-02-04GOMES, Daniel Henrique Ferreira. Desenvolvimento de um aplicativo móvel para predição de mutações patogênicas. 2022. 53 f. Trabalho de Conclusão de Curso (Graduação em Ciência da Computação) – Departamento de Informática e Matemática Aplicada, Universidade Federal do Rio Grande do Norte, Natal, 2022.https://repositorio.ufrn.br/handle/123456789/46801The identification of pathogenic mutations is a real challenge in medicine, therefore, there are several predictors on the market that have different precisions and present different results for the same mutation, which can cause confusion for the physician who seeks to identify whether a mutation is pathogenic or not. . Using decision trees and supervised machine learning algorithms to perform this identification proved to be quite efficient, but there are no clinical applications that use these techniques to predict the pathogenicity of a variant of unknown significance (VUS). Thus, this work presents the DtreePred application, a multiplatform application, natively compiled for Android, iOS and web, which helps the user to identify pathogenic mutations in clinical practice. The application allows you to make requests for predictions of genetic variants in an intuitive and fast way.árvore de decisãodecision treemutação patogênicapathogenics mutationprediçãopredictionbioinformáticabioinformaticsaplicativo móvelmobile appaprendizado de máquinamachine learningbachelorThesis