Análise e classificação da expressão gênica durante o Eritema Nodoso Hansênico através de dados de microarranjo

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: Almeida, Karla Lopes de
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Rio de Janeiro
Brasil
Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia
Programa de Pós-Graduação em Engenharia Biomédica
UFRJ
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/11422/10113
Resumo: Leprosy is a chronic infectious disease that affects millions of people around the world. Currently, the identification of leprosy reactions is of importance in researching because it is a neglected disease. These reactions are the ones that cause the greatest sequels in people with leprosy. However, a diagnostic method that predicts the reaction has not yet been obtained, prior to the appearance of the signs and symptoms. The objective of this work is to propose a methodology to analyze and classify the gene expression present during the reaction stage Erythema Nodosum Leprosum (ENL) through microarray data. For the application of this methodology, data of the expression levels of microarray experiments with ENL and Lepromatous Leprosy (LL) patients were used. We used three statistical techniques (Student’s t test with fold-change, Differential Method - Principal Components Analisys (DMPCA) and linear model microarray analisys-limma), to identify 53 differentially expressed genes. These genes were considered the most significant for the classification between the ENH and LL groups and they were used to create a predictive mathematical model using logistic regression modeling. Our methodology identified genes that were previously described in the literature. The logistic regression model proposed using these genes proved to be efficient when used in a new dataset. The model presented an accuracy of 92% and a specificity of 83.3%.Também disponível on-line.