Microarray technology has become a dynamic tool in gene expression analysis because it allows for the simultaneous measurement of thousands of gene expressions. Uniqueness in experimental units and microarray data platforms, coupled with how gene expressions are obtained, make the field open for interesting research questions. In this dissertation, we present our investigations of two independent studies related to microarray data analysis. First, we study a recent platform in biology and bioinformatics that compares the quality of genetic information from exfoliated colonocytes in fecal matter with genetic material from mucosa cells within the colon. Using the intraclass correlation coe�cient (ICC) as a measure of reproducibility, we assess the reliability of density estimation obtained from preliminary analysis of fecal and mucosa data sets. Numerical findings clearly show that the distribution is comprised of two components. For measurements between 0 and 1, it is natural to assume that the data points are from a beta-mixture distribution. We explore whether ICC values should be modeled with a beta mixture or transformed first and fit with a normal mixture. We find that the use of mixture of normals in the inverse-probit transformed scale is less sensitive toward model mis-specification; otherwise a biased conclusion could be reached. By using the normal mixture approach to compare the ICC distributions of fecal and mucosa samples, we observe the quality of reproducible genes in fecal array data to be comparable with that in mucosa arrays. For microarray data, within-gene variance estimation is often challenging due to the high frequency of low replication studies. Several methodologies have been developed to strengthen variance terms by borrowing information across genes. However, even with such accommodations, variance may be initiated by the presence of outliers. For our second study, we propose a robust modification of optimal shrinkage variance estimation to improve outlier detection. In order to increase power, we suggest grouping standardized data so that information shared across genes is similar in distribution. Simulation studies and analysis of real colon cancer microarray data reveal that our methodology provides a technique which is insensitive to outliers, free of distributional assumptions, effective for small sample size, and data adaptive.

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Tesis por compendio / [ES] Esta tesis se enfoca en el desarrollo de dispositivos inteligentes para detectar metabolitos de sustancias tóxicas y la sobreexpresión enzimática relacionada con enfermedades. Se aborda la limitación de las técnicas de diagnóstico convencionales y se destaca la utilidad de la orina como muestra biológica. Se discuten conceptos de materiales mesoporosos de sílice, reconocimiento molecular y sondas moleculares fluorogénicas. Los objetivos se detallan, y luego se presenta un nanodispositivo para detectar ácido trans,trans-mucónico (t,t-MA) en orina (S4). Se describe una sonda fluorogénica en el infrarrojo cercano para la detección de elevados niveles de alanina aminopeptidasa en orina (NB-ALA). También se presentan nuevas sondas para detectar biomarcadores de cáncer (NB-SO3-Leu y NB-SO3-Ala), resaltando la importancia de su solubilidad y eliminación renal. Finalmente, se propone una sonda molecular no invasiva (Cy7-MAO) para detectar la enzima monoamina oxidasa a través de medidas de fluorescencia en la orina. Las conclusiones subrayan el potencial de estos sistemas para el diagnóstico y tratamiento de enfermedades. / [CA] Aquesta tesi s'enfoca en el desenvolupament de dispositius intel·ligents per detectar metabòlits de substàncies tòxiques i la sobreexpressió enzimàtica relacionada amb malalties. S'aborda la limitació de les tècniques de diagnòstic convencionals i destaca la utilitat de l'orina com a mostra biològica. Es discuteixen conceptes de materials mesoporosos de sílice, reconeixement molecular i sondes moleculars fluorogèniques. Els objectius es detallen, i després es presenta un nanodispositiu per detectar àcid trans, transmucònic (t, t-MA) en orina (S4). Es descriu una sonda fluorogènica a l'infraroig proper per a la detecció d'elevats nivells d'alanina aminopeptidasa en orina (NB-ALA). També es presenten noves sondes per detectar biomarcadors de càncer (NB-SO3-Leu i NB-SO3-Ala), ressaltant la importància de la seva solubilitat i eliminació renal. Finalment, es proposa una sonda molecular no invasiva (Cy7-MAO) per detectar l'enzim monoamina oxidasa a través de mesures de fluorescència a l'orina. Les conclusions subratllen el potencial d'aquests sistemes per al diagnòstic i el tractament de malalties. / [EN] This PhD thesis focuses on the development of smart devices for detecting metabolites of toxic substances and enzyme overexpression related to diseases. It addresses the limitations of conventional diagnostic techniques and highlights the usefulness of urine as a biological sample. Concepts of mesoporous silica materials, molecular recognition, and fluorogenic molecular probes are discussed. The objectives are outlined, and then a nanodevice for detecting trans, trans-muconic acid (t,t-MA) in urine (S4) is presented. A near-infrared fluorogenic probe for the detection of high levels of alanine aminopeptidase in urine (NB-ALA) is described. New probes for detecting cancer biomarkers (NB-SO3-Leu and NB-SO3-Ala) are also introduced, emphasizing the importance of their solubility and renal elimination. Finally, a non-invasive molecular probe (Cy7-MAO) is proposed for detecting monoamine oxidase enzyme through fluorescence measurements in urine. The conclusions underscore the potential of these systems for the diagnosis and treatment of diseases. / Domínguez Rodríguez, M. (2023). Development of Smart Devices for the Detection of Metabolites of Toxic Substances and Disease-Related Enzyme Overexpression [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202015 / Compendio