Thesis (Ph.D)--Biology, Georgia Institute of Technology, 2009. / Committee Chair: Mark Borodovky; Committee Member: Jung H. Choi; Committee Member: King Jordan; Committee Member: Leonid Bunimovich; Committee Member: Yury Chernoff. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Made available in DSpace on 2014-12-17T14:55:50Z (GMT). No. of bitstreams: 1 NielsenCD_DISSERT.pdf: 3425927 bytes, checksum: 2a460ebc6b49fe832a4f35b40786bc47 (MD5) Previous issue date: 2010-12-20 / Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and R?nyi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations / Os m?todos convencionais para resolver o problema de separa??o cega de fontes n?o lineares em geral utilizam uma s?rie de restri??es ? obten??o da solu??o, levando muitas vezes a uma n?o perfeita separa??o das fontes originais e alto custo computacional. Neste trabalho, prop?e-se uma alternativa de medida de independ?ncia com base na teoria da informa??o e utilizam-se ferramentas da intelig?ncia artificial para resolver problemas de separa??o cega de fontes lineares e posteriormente n?o lineares. No modelo linear aplica-se algoritmos gen?ticos e a Negentropia de R?nyi como medida de independ?ncia para encontrar uma matriz de separa??o linear a partir de misturas lineares usando sinais de forma de ondas, ?udios e imagens. Faz-se uma compara??o com dois tipos de algoritmos de An?lise de Componentes Independentes bastante difundidos na literatura. Posteriormente, utiliza-se a mesma medida de independ?ncia como fun??o custo no algoritmo gen?tico para recuperar sinais de fontes que foram misturadas por fun??es n?o lineares a partir de uma rede neural artificial do tipo base radial. Algoritmos gen?ticos s?o poderosas ferramentas de pesquisa global e, portanto, bem adaptados para utiliza??o em problemas de separa??o cega de fontes. Os testes e as an?lises se d?o atrav?s de simula??es computacionais

Anxiety and depression are highly prevalent diseases with common heritable elements, but the particular genetic mechanisms and biological pathways underlying them are poorly understood. Part of the challenge in understanding the genetic basis of these disorders is that they are polygenic and often context-dependent. In my thesis, I apply a series of modern statistical tools to ascertain some of the myriad genetic and environmental factors that underlie fear-related behaviours in nearly two thousand heterogeneous stock mice, which serve as animal models of anxiety and depression. Using a Bayesian method called Sparse Partitioning and a frequentist method called Bagphenotype, I identify gene-by-sex interactions that contribute to variation in fear-related behaviours, such as those displayed in the elevated plus maze and the open field test, although I demonstrate that the contributions are generally small. Also using Bagphenotype, I identify hundreds of gene-by-environment interactions related to these traits. The interacting environmental covariates are diverse, ranging from experimenter to season of the year. With gene expression data from a brain structure associated with anxiety called the hippocampus, I generate modules of co-expressed genes and map them to the genome. Two of these modules were enriched for key nervous system components — one for dendritic spines, another for oligodendrocyte markers — but I was unable to find significant correlations between them and fear-related behaviours. Finally, I employed another Bayesian technique, Sparse Instrumental Variables, which takes advantage of conditional probabilities to identify hippocampus genes whose expression appears not just to be associated with variation in fear-related behaviours, but cause variation in those phenotypes.