Suárez Gómez, Deiver

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    Tamaño de muestra necesario para que la estimación de intervalos Frecuentista y Bayesiano coincidan dentro de un error predeterminado
    (2018-11-30) Suárez Gómez, Deiver; Rolke, Wolfgang A.; College of Arts and Sciences - Sciences; Lorenzo González, Edgardo; Santana Morant, Dámaris; Department of Mathematics; Bartolomei Suárez, Sonia M.
    Por lo general, se acepta que a medida que aumenta el tamaño de un conjunto de datos, el efecto de la distribución previa en el intervalo de credibilidad Bayesiano disminuye y estos intervalos se acercan a los correspondientes intervalos de confianza Frecuentista. En esta tesis, se estudia la pregunta de cuán grande es el tamaño de muestra que se necesita para tener cierta certeza de que los intervalos de confianza y los intervalos de credibilidad sean similares dentro de un error prescrito. Esto, con el propósito de afirmar cual es el tamaño de muestra necesario para que los dos enfoques de la estadística coincidan en la estimación por intervalos. Hay respuestas explícitas para varios casos estándar como la estimación de la media y la desviación estándar de una distribución Normal, la probabilidad de éxito en los ensayos de Bernoulli y las tasas de la distribución de Poisson, Geométrica y Exponencial. Para este trabajo se desarrolló una calculadora en línea que está disponible en https://server-deiver .shinyapps.io/sample_size_frequentist_and_bayesian/. Ésta calcula intervalos de confianza, intervalos de credibilidad y el tamaño de muestra necesario para que estos intervalos sean iguales dentro de un error predeterminado. La calculadora no requiere que el usuario tenga algún conocimiento de R.
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    Gene expression commonalities between autism and schizophrenia via biooptimatics
    (2023-09-19) Suárez Gómez, Deiver; Isaza Brando, Clara E.; College of Engineering; Cabrera Ríos, Mauricio; González Méndez, Ricardo; Latorre Esteves, Magda; Pérez Morales, Jaileene; Bioengineering Program; Zapata Medina, Rocío
    In the past, schizophrenia and autism spectrum disorders were diagnosed as a single disorder, but they are now recognized as separate and distinct conditions due to their different symptoms and age of onset. Nevertheless, similarities between schizophrenia and autism spectrum disorders have been found, such as the sharing of genetic information and the absence of typical behaviors reflecting the deterioration of social cognition. Despite these findings, the etiology of both disorders remains uncertain and there is still no objective diagnosis or effective cure. This study aims to help explain the common genetics between both conditions through gene expression data analysis using optimization methods. For this, public case-control studies of blood and brain tissue samples from autism and schizophrenia were examined. An R software package called Optimization-Based Analysis of Micro Arrays (OBAMA) was developed, which includes multiple criteria optimization (MCO), minimum spanning tree (MST), and -for the first time- optimal group formation (OGF) methodologies. With OBAMA, the following analyses were performed: 1) individual analysis of individual datasets, taking into account the characterization of sex to minimize sex bias; 2) meta-analysis of datasets, with the same consideration of sex characterization; 3) maximum correlation structures of individual and meta-analyses; and 4) formation of groups of genes and biological processes in optimal global conditions. The OBAMA package has the advantage of being portable and able to be run on modest computer hardware, such as personal computers. With its use, this study identified the following genes for autism spectrum disorder and schizophrenia: two genes (VNN2, PLGLB1) in blood samples, four genes in brain tissue (HSPA6, RGS1, RNU4_2, CCL4) and a common gene in blood and brain tissue (S100A8). In addition, signaling pathways involved in inflammation, ribosome, metabolism, and cancer were proposed, among others. Finally, we propose groups of genes involved in different biological processes, including metabolic processes, signals, cell communication, responses to stimuli, among others. The results set, which includes genes, pathways, and biological processes, could be crucial in helping to understand the underlying etiology of these two conditions.