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Identification of causality in genetics and neuroscience

Ribeiro, Adèle Helena

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Matemática e Estatística 2018-11-28

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  • Título:
    Identification of causality in genetics and neuroscience
  • Autor: Ribeiro, Adèle Helena
  • Orientador: Fujita, André; Soler, Julia Maria Pavan
  • Assuntos: Aprendizagem De Estrutura; Redes Funcionais Cerebrais; Modelos Gráficos Probabilísticos; Modelo Misto Poligênico; Causalidade De Granger; Granger Causality; Functional Brain Networks; Polygenic Mixed Model; Probabilistic Graphical Models; Structure Learning
  • Notas: Tese (Doutorado)
  • Descrição: Causal inference may help us to understand the underlying mechanisms and the risk factors of diseases. In Genetics, it is crucial to understand how the connectivity among variables is influenced by genetic and environmental factors. Family data have proven to be useful in elucidating genetic and environmental influences, however, few existing approaches are able of addressing structure learning of probabilistic graphical models (PGMs) and family data analysis jointly. We propose methodologies for learning, from observational Gaussian family data, the most likely PGM and its decomposition into genetic and environmental components. They were evaluated by a simulation study and applied to the Genetic Analysis Workshop 13 simulated data, which mimic the real Framingham Heart Study data, and to the metabolic syndrome phenotypes from the Baependi Heart Study. In neuroscience, one challenge consists in identifying interactions between functional brain networks (FBNs) - graphs. We propose a method to identify Granger causality among FBNs. We show the statistical power of the proposed method by simulations and its usefulness by two applications: the identification of Granger causality between the FBNs of two musicians playing a violin duo, and the identification of a differential connectivity from the right to the left brain hemispheres of autistic subjects.
  • DOI: 10.11606/T.45.2019.tde-15032019-190109
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Matemática e Estatística
  • Data de criação/publicação: 2018-11-28
  • Formato: Adobe PDF
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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