Sergio Rodrigues de Morais

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OBJECTIVES We propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of nasopharyngeal carcinoma (NPC) on a case-control epidemiologic study that consists of 1289 subjects and 150 risk factors. METHODS This framework builds on the use of Bayesian networks(More)
BACKGROUND The aim of this study was to provide a framework for the analysis of visceral obesity and its determinants in women, where complex inter-relationships are observed among lifestyle, nutritional and metabolic predictors. Thirty-four predictors related to lifestyle, adiposity, body fat distribution, blood lipids and adipocyte sizes have been(More)
In this paper, we discuss simple methods for identification and handling of almost-deterministic relationships (ADR) in automatic constraint-based Bayesian network structure discovery. The problem with ADR is that conditional independence tests become unreliable when the conditional set almost-determine one of the variables in the test. Such errors have(More)
In this study, we discuss and apply a novel and efficient algorithm for learning a local Bayesian network model in the vicinity of the ZNF217 oncogene from breast cancer microarray data without having to decide in advance which genes have to be included in the learning process. ZNF217 is a candidate oncogene located at 20q13, a chromosomal region frequently(More)
In this paper, we propose a novel constraint-based Markov boundary discovery algorithm, called MBOR, that scales up to hundreds of thousands of variables. Its correctness under faithfulness condition is guaranteed. A thorough empiric evaluation of MBOR's robust-ness, efficiency and scalability is provided on synthetic databases involving thousands of(More)