University, Diss., 2005--Bremen. / Erscheinungsjahr an der Haupttitelstelle: 2004.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (leaves 122-146).

Thesis (Ph.D.)--University of Minnesota, 1999. / Includes bibliographical references (leaves 179-187). Also available on the World Wide Web as a PDF file.

Universiẗat, Diss., 2004--Kassel.

Universiẗat, Diss., 2002--Saarbrücken.

Techn. University, Diss., 2006--Darmstadt.

abstract: As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable. / Dissertation/Thesis / M.S. Computer Science 2011

A taxa de mortalidade infantil é um dos indicadores mais usados para medir a qualidade de vida da população. Um dos indicadores sócio-econômico do Rio Grande do Sul é o Índice de Desenvolvimento Sócio-econômico (IDESE) da Fundação de Economia e Estatística (FEE) que tem como um de seus componentes a taxa de mortalidade infantil. Geralmente os estudos relacionam a taxa de mortalidade infantil com fatores de risco associados às áreas em estudo de forma descritiva, ou seja, de forma apenas visual através de mapas. O presente trabalho apresenta uma aplicação de um dos métodos de Epidemiologia Espacial: Estudos de Correlação Ecológica, através de modelos hierárquicos e métodos inteiramente Bayesianos, utilizando covariáveis. Os principais problemas presentes nas taxas de mortalidade brutas ou nas SMR (Standardised Mortality Ratio) como a auto-correlação espacial e a instabilidade dos estimadores para pequenas áreas são discutidos. Para superar estas dificuldades as estimativas do risco relativo obtidas pela análise de regressão espacial, utilizando modelagem inteiramente Bayesiana, são apresentados como alternativa, pois além de incorporar componente espacialmente estruturado ao modelo, permite também a inclusão de covariáveis. No artigo são analisados os riscos de mortalidade infantil nos 496 municípios do Rio Grande do Sul para dados acumulados entre os anos de 2001 a 2004. Foram comparados vários modelos com diferentes especificações de componente espacial e covariáveis provenientes do IDESE-FEE/2003. Verificou-se que os modelos que utilizam a estrutura espacial além de covariáveis apresentaram melhor performance, quando comparado pelo critério DIC (Deviance Information Criterion). Comparando as SMRs com os riscos relativos obtidos pela modelagem inteiramente Bayesiana foi possível observar um ganho substancial na interpretação e na detecção de padrões de variação no risco de mortalidade infantil nos municípios do Rio Grande do Sul. / The infant mortality rate is one of the indicators used to measure the population’s life quality. The Rio Grande do Sul State has a social and economic indicator called Índice de Desenvolvimento Sócio-econômico (IDESE), maintained by the Economic and Statistics Foundation (FEE), which also uses the infant mortality rate. Usually, most studies relate the infant mortality rate with risk factors visually, aided by maps. This study presents the methodology and an application of one of the Spatial Epidemiology methods, the Ecologic Correlation, using Hierarchical Bayesian procedures. The main problems found in Ecologic correlations, such as the spatial autocorrelation and the estimator’s instability for small areas, are discussed. To overcome these difficulties, the relative risk estimate obtained by spatial regression analysis using fully Bayesian estimation method is presented. Presently, the rate of infant mortality is analysed in all 496 municipalities of the Rio Grande do Sul State, between the years 2001 to 2004. Several models with different specifications of spatial components and different variables from the IDESE-FEE/2003 were compared. It was found that the model with spatial structure and the Education variable showed better performance than other models. With this methodology was possible to obtain a more interpretable pattern of infant mortality risk in the Rio Grande do Sul State.

A taxa de mortalidade infantil é um dos indicadores mais usados para medir a qualidade de vida da população. Um dos indicadores sócio-econômico do Rio Grande do Sul é o Índice de Desenvolvimento Sócio-econômico (IDESE) da Fundação de Economia e Estatística (FEE) que tem como um de seus componentes a taxa de mortalidade infantil. Geralmente os estudos relacionam a taxa de mortalidade infantil com fatores de risco associados às áreas em estudo de forma descritiva, ou seja, de forma apenas visual através de mapas. O presente trabalho apresenta uma aplicação de um dos métodos de Epidemiologia Espacial: Estudos de Correlação Ecológica, através de modelos hierárquicos e métodos inteiramente Bayesianos, utilizando covariáveis. Os principais problemas presentes nas taxas de mortalidade brutas ou nas SMR (Standardised Mortality Ratio) como a auto-correlação espacial e a instabilidade dos estimadores para pequenas áreas são discutidos. Para superar estas dificuldades as estimativas do risco relativo obtidas pela análise de regressão espacial, utilizando modelagem inteiramente Bayesiana, são apresentados como alternativa, pois além de incorporar componente espacialmente estruturado ao modelo, permite também a inclusão de covariáveis. No artigo são analisados os riscos de mortalidade infantil nos 496 municípios do Rio Grande do Sul para dados acumulados entre os anos de 2001 a 2004. Foram comparados vários modelos com diferentes especificações de componente espacial e covariáveis provenientes do IDESE-FEE/2003. Verificou-se que os modelos que utilizam a estrutura espacial além de covariáveis apresentaram melhor performance, quando comparado pelo critério DIC (Deviance Information Criterion). Comparando as SMRs com os riscos relativos obtidos pela modelagem inteiramente Bayesiana foi possível observar um ganho substancial na interpretação e na detecção de padrões de variação no risco de mortalidade infantil nos municípios do Rio Grande do Sul. / The infant mortality rate is one of the indicators used to measure the population’s life quality. The Rio Grande do Sul State has a social and economic indicator called Índice de Desenvolvimento Sócio-econômico (IDESE), maintained by the Economic and Statistics Foundation (FEE), which also uses the infant mortality rate. Usually, most studies relate the infant mortality rate with risk factors visually, aided by maps. This study presents the methodology and an application of one of the Spatial Epidemiology methods, the Ecologic Correlation, using Hierarchical Bayesian procedures. The main problems found in Ecologic correlations, such as the spatial autocorrelation and the estimator’s instability for small areas, are discussed. To overcome these difficulties, the relative risk estimate obtained by spatial regression analysis using fully Bayesian estimation method is presented. Presently, the rate of infant mortality is analysed in all 496 municipalities of the Rio Grande do Sul State, between the years 2001 to 2004. Several models with different specifications of spatial components and different variables from the IDESE-FEE/2003 were compared. It was found that the model with spatial structure and the Education variable showed better performance than other models. With this methodology was possible to obtain a more interpretable pattern of infant mortality risk in the Rio Grande do Sul State.

abstract: Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state. / Dissertation/Thesis / Ph.D. Biological Design 2012