2 research outputs found
Gene Network Biological Validity Based on Gene-Gene Interaction Relevance
In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown
PrevalĂŞncia de papilomavĂrus humano no câncer de mama e apoio ao diagnĂłstico de câncer de mama pelas redes bayesianas: revisĂŁo sistemática e metanálise
Tese de Doutorado apresentada ao Programa de PĂłs-Graduação em CiĂŞncias da SaĂşde da Universidade do Extremo Sul Catarinense – UNESC, para obtenção do tĂtulo de Doutor em CiĂŞncias da SaĂşde.Introdução: O Câncer de Mama Ă© o mais comum entre as mulheres. Estudos epidemiolĂłgicos com enfoque em fatores de risco e diagnĂłstico sĂŁo importantes na prevenção e detecção precoce podendo aumentar a probabilidade de sucesso no tratamento e recuperação. Durante as Ăşltimas duas dĂ©cadas alguns estudos tĂŞm investigado a possibilidade de associação do PapilomavĂrus Humano (HPV) ao Câncer de Mama, assim como o uso de inteligĂŞncia artificial tornou-se amplamente aceito em aplicações mĂ©dicas, e entre suas atuações, as Redes Bayesianas tĂŞm sido utilizadas como mĂ©todo preciso e nĂŁo invasivo no apoio ao diagnĂłstico de diversas neoplasias incluindo o Câncer de Mama. Objetivo: Determinar a acurácia das Redes Bayesianas no apoio ao diagnĂłstico de Câncer de Mama (Artigo 1); e determinar a prevalĂŞncia do HPV no Câncer de Mama (Artigo 2). Metodologia: RevisĂŁo Sistemática e Metanálise. A estratĂ©gia de busca foi realizada fazendo-se uma pesquisa exaustiva nas bases de dados Medline, Cancerlit, Lilacs, Embase, Scopus, Cochrane, IBECS, BIOSIS, Web of Science e Literatura Cinza, por publicações realizadas entre janeiro de 1990 e janeiro de 2012 (Artigo 1), e janeiro de 1990 e janeiro de 2011 (Artigo 2). Foram incluĂdos estudos primários de acurácia diagnĂłstica prospectivos ou retrospectivos, transversais, que avaliaram lesões de mama (condições alvo) por meio das Redes Bayesianas (teste em avaliação) (Artigo 1); e de caso-controle ou transversais, prospectivos ou retrospectivos, que avaliaram a prevalĂŞncia do HPV em lesões mamárias (Artigo 2). A metanálise foi desenvolvida nos softwares Meta-DiSc® v.1.4 e RevMan 5.0.21. Resultados: No Artigo 1, quatro estudos primários, incluindo 1204 lesões mamárias foram analisados; a prevalĂŞncia de Câncer de Mama foi 40,03%; 90% (437/482) dos casos de Câncer de Mama e 6,51% (47/722) dos casos de lesões benignas foram positivos nas Redes Bayesianas; um resultado positivo das Redes Bayesianas aumentou a probabilidade de ocorrĂŞncia do um Verdadeiro Positivo de 40,03% para 90,05% (IC 95%, 90,0%-90,1%) e um resultado negativo para as Redes Bayesianas diminuiu a probabilidade de ocorrĂŞncia de um Falso Positivo de 40,03% para 6,44% (IC 95%, 6,40%-6,48%); a área sob a curva SROC foi de 0,97, com um valor de ponto Q* de 0,93. No Artigo 2, foram incluĂdos 29 estudos primários, com um total de 2.211 amostras. A prevalĂŞncia geral do HPV em pacientes com Câncer de Mama foi de 23,0% (IC 95%, 21,2%-24,8%), e variou de 13,4% (IC 95%, 10,2%-16%) na Europa para 42,9% (IC 95%, 36,4%-49,4%) na AmĂ©rica do Norte e Austrália. A prevalĂŞncia de HPV nos controles foi de 12,9%. As combinações dos nove estudos de caso-controle mostrou que o Câncer de Mama foi associado ao HPV (Odds Ratio 5,9-IC 95%, 3,26-10,67). Conclusões: No Artigo 1, a probabilidade prĂ©-teste aumentou de 40,03% para 90,05% sendo positivo para lesões malignas diagnosticadas pela Rede Bayesiana, e diminuiu para 6,44% para um resultado negativo; assim, nossos resultados mostraram que as Redes Bayesianas representam um mĂ©todo preciso e nĂŁo invasivo de apoio ao diagnĂłstico de Câncer de Mama. No Artigo 2 encontramos uma alta prevalĂŞncia de HPV no Câncer de Mama. Há fortes evidĂŞncias para sugerir que o HPV tenha um papel importante no desenvolvimento desse tipo de câncer.Introduction: Breast Cancer is the most common among women. Epidemiological studies focusing on risk factors and diagnosis are important in the prevention and early detection can increase the likelihood of successful treatment and recovery. During the last two decades few studies have investigated the possible association of human papillomavirus (HPV) to Breast Cancer, as the use of artificial intelligence has become widely accepted in medical applications, and between their applications, the Bayesian Networks have been used as a noninvasive and accurate method to support diagnosis of various cancers including Breast Cancer. Objective: To determine the accuracy of Bayesian Networks to support diagnosis of Breast Cancer (Article 1) and to determine the prevalence of HPV in Breast Cancer (Article 2). Methods: Systematic review and meta-analysis. The search strategy was performed by making an exhaustive search in Medline, CancerLit, Lilacs, Embase, Scopus, Cochrane, IBECS, BIOSIS, Web of Science and Literature Gray, for publications between January 1990 and January 2012 (Article 1) and January 1990 and January 2011 (Article 2). We included primary studies of diagnostic accuracy prospective or retrospective, cross-sectional breast lesions (target conditions) by Bayesian Networks (index test) (Article 1), and case-control or cross-sectional, prospective or retrospective, which assessed the prevalence of HPV in breast lesions (Article 2). The meta-analysis was developed in the Meta-Disc ® software v.1.4 and RevMan 5.0.21. Results: In Article 1, four primary studies, including 1204 breast lesions were analyzed, the prevalence of Breast Cancer was 40.03%, 90% (437/482) of cases of Breast Cancer and 6.51% (47/722) of the cases of benign lesions were positive on Bayesian Networks; a positive result of Bayesian Networks increased the probability of a True Positive of 40.03% to 90.05% (95% CI, 90.0%-90.1%) and a negative result for Bayesian Networks decreased the likelihood occurrence of a false positive of 40.03% to 6.44% (95% CI, 6.40%-6.48%), the area under the curve SROC was 0.97, with a value of point Q* 0.93. In Article 2, 29 primary studies were included, with a total of 2211 samples. The overall prevalence of HPV in patients with Breast Cancer was 23.0% (95% CI, 21.2% -24.8%), and ranged from 13.4% (95% CI, 10.2% - 16%) in Europe to 42.9% (95% CI, 36.4% -49.4%) in North America and Australia. The prevalence of HPV in controls was 12.9%. The combinations of the nine case-control studies showed that Breast Cancer was associated with HPV (odds ratio 5.9, 95% CI 3.26 to 10.67). Conclusions: In Article 1, the pretest probability increased from 40.03% to 90.05% being positive for malignant lesions diagnosed by Bayesian Network, and decreased to 6.44% for a negative result, so our results showed that Bayesian Networks represent an accurate and noninvasive diagnostic support of Breast Cancer. In Article 2, we found a high prevalence of HPV in Breast Cancer. There is strong evidence to suggest that HPV has an important role in the development of this cancer