Tesi sul tema "Microarray"
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1
Pernagallo, Salvatore. "Biocompatible polymer microarrays for cellular high-content screening". Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/7571.
Testo completoAbstract (sommario):
The global aim of this thesis was to study the use of microarray technology for the screening and identification of biocompatible polymers, to understand physiological phenomena, and the design of biomaterials, implant surfaces and tissue-engineering scaffolds. This work was based upon the polymer microarray platform developed by the Bradley group. Polymer microarrays were successfully applied to find the best polymer supports for: (i) mouse fibroblast cells and used to evaluate cell biocompatibility and cell morphology. Fourteen polyurethanes demonstrated significant cellular adhesion. (ii) Analysis of the adhesion of human erythroleukaemic K562 suspension cells onto biomaterials with particular families of polyurethanes and polyacrylates identified. A DNA microarray study (to access the global gene expression profiles upon cellular binding) demonstrated that interactions between cells and some polyacrylates induced a number of transcriptomic changes. These results suggested that, during these interactions, a chain of cellular changes is triggered, most notably resulting in the downregulation of membrane receptors and ligands. (iii) Identification of polymers with potential applications in the field of stem cell biology. Polymers were identified that showed attachment, promotion and stabilisation of hepatocyte-like cells. A polyurethane support (PU-134) was pinpointed, which significantly improved both hepatocyte-like cell function and “lifespan”. A second project investigated biomaterials that promoted adhesion, growth and function of endothelial progenitor cells. A new polymer matrix was identified which contained the necessary signals to promote endothelial phenotype and function. This has potential application in the creation of blood vessels and the endothelialisation of artificial vessel prostheses and stent coatings for improving angioplasty therapy. (iv) The study of bacterial adhesion, focusing on the adhesion of food-borne pathogenic bacterium Salmonella enterica serovar typhimurium, strain SL1344, and the commensal bacterium Escherichia coli, strain W3110. Several polymers were found to support selective bacterial enrichment, as well as others that minimised bacterial adhesion.
2
Wang, Tao. "Statistical design and analysis of microarray experiments". Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains ix, 146 p.; also includes graphics (some col.) Includes bibliographical references (p. 145-146). Available online via OhioLINK's ETD Center
3
Klenkar, Goran. "Protein Microarray Chips". Doctoral thesis, Linköping : Univ, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8904.
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4
Harness, Denise. "A Comparison of Unsupervised Methods for DNA Microarray Leukemia Data". Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/106.
Testo completoAbstract (sommario):
Advancements in DNA microarray data sequencing have created the need for sophisticated machine learning algorithms and feature selection methods. Probabilistic graphical models, in particular, have been used to identify whether microarrays or genes cluster together in groups of individuals having a similar diagnosis. These clusters of genes are informative, but can be misleading when every gene is used in the calculation. First feature reduction techniques are explored, however the size and nature of the data prevents traditional techniques from working efficiently. Our method is to use the partial correlations between the features to create a precision matrix and predict which associations between genes are most important to predicting Leukemia diagnosis. This technique reduces the number of genes to a fraction of the original. In this approach, partial correlations are then extended into a spectral clustering approach. In particular, a variety of different Laplacian matrices are generated from the network of connections between features, and each implies a graphical network model of gene interconnectivity. Various edge and vertex weighted Laplacians are considered and compared against each other in a probabilistic graphical modeling approach. The resulting multivariate Gaussian distributed clusters are subsequently analyzed to determine which genes are activated in a patient with Leukemia. Finally, the results of this are compared against other feature engineering approaches to assess its accuracy on the Leukemia data set. The initial results show the partial correlation approach of feature selection predicts the diagnosis of a Leukemia patient with almost the same accuracy as using a machine learning algorithm on the full set of genes. More calculations of the precision matrix are needed to ensure the set of most important genes is correct. Additionally more machine learning algorithms will be implemented using the full and reduced data sets to further validate the current prediction accuracy of the partial correlation method.
5
Dvergsten, Erik C. "A Weighted Gene Co-expression Network Analysis for Streptococcus sanguinis Microarray Experiments". VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4430.
Testo completoAbstract (sommario):
Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes.
Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene modules were determined from the connection strengths, and various network connectivity measures were calculated.
S. sanguinis gene expression profile data was loaded for 2272 genes and 14 samples with 3 replicates each. The soft thresholding power β=6 was chosen to maximize R2 while maintaining a high mean number of connections. Nine modules were found. Possible meta-modules were found to be: Module 1: Blue & Green, Module 2: Pink, Module 3: Yellow, Brown & Red, Module 4: Black, Module 5: Magenta & Turquoise. The absolute value of module membership was found to be highly positively correlated with intramodular connectivity. Each of the nine modules were examined. Two methods (intramodular connectivity and TOM-based connectivity followed by network mapping) for identifying candidate hub genes were performed. Most modules provided similar results between the two methods. Similar rankings between the two methods can be considered equivalent and both can be used to detect candidate hub genes. Gene ontology information was unavailable to help select a module of interest. This network analysis would help researchers create new research hypotheses and design experiments for validation of candidate hub genes in biologically important modules.
6
Hernández-Cabronero, Miguel. "DNA Microarray Image Compression". Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/297706.
Testo completoAbstract (sommario):
En los experimentos con DNA microarrays se genran dos imágenes monocromo, las cuales es conveniente almacenar para poder realizar análisis más precisos en un futuro. Por tanto, la compresión de imágenes surge como una herramienta particularmente útil para minimizar los costes asociados al almacenamiento y la transmisión de dichas imágenes. Esta tesis tiene por objetivo mejorar el estado del arte en la compresión de imágenes de DNA microarrays.
Como parte de esta tesis, se ha realizado una detallada investigación de las características de las imágenes de DNA microarray. Los resultados experimentales indican que los algoritmos de compresión no adaptados a este tipo de imágenes producen resultados más bien pobres debido a las características de estas imágenes. Analizando las entropías de primer orden y condicionales, se ha podido determinar un límite aproximado a la compresibilidad sin pérdida de estas imágenes. Aunque la compresión basada en contexto y en segmentación proporcionan mejoras modestas frente a algoritmos de compresión genéricos, parece necesario realizar avances rompedores en el campo de compresión de datos para superar los ratios 2:1 en la mayor parte de las imágenes.
Antes del comienzo de esta tesis se habían propuesto varios algoritmos de compresión sin pérdida con rendimientos cercanos al límite óptimo anteriormente mencionado. Sin embargo, ninguno es compatible con los estándares de compresión existentes. Por tanto, la disponibilidad de descompresores compatibles en plataformas futuras no está garantizado. Además, la adhesión a dichos estándares se require normalmente en escenarios clínicos. Para abordar estos problemos, se propone una transformada reversible compatible con el standard JPEG2000: la Histogram Swap Transform (HST). La HST mejora el rendimiento medio de JPEG2000 en todos los corpora entre 1.97% y 15.53%. Además, esta transformada puede aplicarse incurriendo en un sobrecoste de tiempo negligible. Con la HST, JPEG2000 se convierte en la alternativa estándard más competitiva a los compresores no estándard. Las similaridades entre imágenes del mismo corpus también se han estudiado para mejorar aún más los resultados de compresión de imágenes de DNA
microarrays. En concreto, se ha encontrado una agrupación óptima de las imágenes que maximiza la correlación dentro de los grupos. Dependiendo del corpus observado, pueden observarse resultados de correlación medios de entre 0.75 y 0.92. Los resultados experimentales obtenidos indican que las técnicas de decorrelación espectral pueden mejorar los resultados de compresión hasta en 0.6 bpp, si bien ninguna de las transformadas es efectiva para todos los corpora utilizados.
Por otro lado, los algoritmos de compresión con pérdida permiten obtener resultados de compresión arbitrarios a cambio de modificar las imágenes y, por tanto, de distorsionar subsiguientes procesos de análisis. Si la distorsión introducida es más pequeña que la variabilidad experimental inherente, dicha distorsión se considera generalmente aceptable. Por tanto, el uso de técnicas de compresión con pérdida está justificado. En esta tesis se propone una métrica de distorsión para imágenes de DNA microarrays capaz de predecir la cantidad de distorsión introducida en el análisis sin necesitar analizar las imágenes modificadas, diferenciando entre cambios importantes y no importantes.
Asimismo, aunque ya se habían propuesto algunos algoritmos de compresión con pérdida para estas imágenes antes del comienzo de la tesis, ninguno estaba específicamente diseñado para minimizar el impacto en los procesos de análisis para un bitrate prefijado. En esta tesis, se propone un compresor con pérdida (el Relative Quantizer (RQ) coder) que mejora los resultados de todos los métodos anteriormente publicados. Los resultados obtenidos sugieren que es posible comprimir con ratios superiores a 4.5:1 mientras se introducen distorsiones en el análisis inferiores a la mitad de la variabilidad experimental inherente. Además, se han propuesto algunas mejoras a dicho compresor, las cuales permiten realizar una codificación lossy-to-lossless (el Progressive RQ (PRQ) coder), pudiéndose así reconstruir una imagen comprimida con diferentes niveles de calidad. Cabe señalar que los resultados de compresión anteriormente mencionados se obtienen con una complejidad computacional ligeramente inferior a la del mejor compresor sin pérdida para imágenes de DNA microarrays.In DNA microarray experiments, two grayscale images are produced. It is convenient to save these images for future, more accurate re-analysis. Thus, image compression emerges as a particularly useful tool to alleviate the associated storage and transmission costs. This dissertation aims at improving the state of the art of the compression of DNA microarray images. A thorough investigation of the characteristics of DNA microarray images has been performed as a part of this work. Results indicate that algorithms not adapted to DNA microarray images typically attain only mediocre lossless compression results due to the image characteristics. By analyzing the first-order and conditional entropy present in these images, it is possible to determine approximate limits to their lossless compressibility. Even though context-based coding and segmentation provide modest improvements over generic-purpose algorithms, conceptual breakthroughs in data coding are arguably required to achieve compression ratios exceeding 2:1 for most images. Prior to the start of this thesis, several lossless coding algorithms that have performance results close to the aforementioned limit were published. However, none of them is compliant with existing image compression standards. Hence, the availability of decoders in future platforms -a requisite for future re-analysis- is not guaranteed. Moreover, the adhesion to standards is usually a requisite in clinical scenarios. To address these problems, a fast reversible transform compatible with the JPEG2000 standard -the Histogram Swap Transform (HST)- is proposed. The HST improves the average compression performance of JPEG2000 for all tested image corpora, with gains ranging from 1.97% to 15.53%. Furthermore, this transform can be applied with only negligible time complexity overhead. With the HST, JPEG2000 becomes arguably the most competitive alternatives to microarray-specific, non-standard compressors. The similarities among sets of microarray images have also been studied as a means to improve the compression performance of standard and microarray-specific algorithms. An optimal grouping of the images which maximizes the inter-group correlation is described. Average correlations between 0.75 and 0.92 are observed for the tested corpora. Thorough experimental results suggest that spectral decorrelation transforms can improve some lossless coding results by up to 0.6bpp, although no single transform is effective for all copora. Lossy coding algorithms can yield almost arbitrary compression ratios at the cost of modifying the images and, thus, of distorting subsequent analysis processes. If the introduced distortion is smaller than the inherent experimental variability, it is usually considered acceptable. Hence, the use of lossy compression is justified on the assumption that the analysis distortion is assessed. In this work, a distortion metric for DNA microarray images is proposed to predict the extent of this distortion without needing a complete re-analysis of the modified images. Experimental results suggest that this metric is able to tell apart image changes that affect subsequent analysis from image modifications that do not. Although some lossy coding algorithms were previously described for this type of images, none of them is specifically designed to minimize the impact on subsequent analysis for a given target bitrate. In this dissertation, a lossy coder -the Relative Quantizer (RQ) coder- that improves upon the rate- distortion results of previously published methods is proposed. Experiments suggest that compression ratios exceeding 4.5:1 can be achieved while introducing distortions smaller than half the inherent experimental variability. Furthermore, a lossy-to-lossless extension of this coder -the Progressive RQ (PRQ) coder- is also described. With the PRQ, images can be compressed once and then reconstructed at different quality levels, including lossless reconstruction. In addition, the competitive rate-distortion results of the RQ and PRQ coders can be obtained with computational complexity slightly smaller than that of the best-performing lossless coder of DNA microarray images.
7
Downes, Aidan Rawle. "Microarray submissions to Experibase". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33281.
Testo completoAbstract (sommario):
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaf 32, first group).
Experibase is an experimental database that supports the storage of data from leading biological experiment techniques. Experibase ontology was extended to include a robust representation of microarray data, a leading experimental technique. The microarray submission system takes advantage of Experibase's new microarray storage capabilities by allowing biologist to submit microarray data to Experibase using an application that they are already familiar with. The transformation of data from the submitted format to a format suitable for Experibase takes place without the submitter's knowledge, reducing the need for an Experibase specific submission application.
by Aidan Rawle Downes.
M.Eng.
8
Mao, Shihong. "Comparative Microarray Data Mining". Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.
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9
Stephens, Nathan Wallace. "A Comparison of Microarray Analyses: A Mixed Models Approach Versus the Significance Analysis of Microarrays". BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1115.
Testo completoAbstract (sommario):
DNA microarrays are a relatively new technology for assessing the expression levels of thousands of genes simultaneously. Researchers hope to find genes that are differentially expressed by hybridizing cDNA from known treatment sources with various genes spotted on the microarrays. The large number of tests involved in analyzing microarrays has raised new questions in multiple testing. Several approaches for identifying differentially expressed genes have been proposed. This paper considers two: (1) a mixed models approach, and (2) the Signiffcance Analysis of Microarrays.
10
Fronczyk, Kassandra M. "Development of Informative Priors in Microarray Studies". Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2031.pdf.
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11
Nowacki, Sandra. "DPC4-Zielgenidentifikation mittels Microarray-Technologie". [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975910027.
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Lau, Kelvin Ee Ming. "Microarray analysis of Acidovorax temperans". Thesis, University of Auckland, 2008. http://hdl.handle.net/2292/5869.
Testo completoAbstract (sommario):
Bacteria belonging to the genus Acidovorax have been shown to be a consistent member of the activated sludge microbial community. Two phenotypic variants of A. temperans CB2 isolated locally from activated sludge exhibit noteworthy characteristics, such as the ability to form biofilms and flocs, which are critical microbial processes underlying all modern wastewater treatment systems. Gene expression microarray technology is a functional genomics platform that enables the simultaneous interrogation of all expressed transcripts during normal cell ontogeny, or in response to specific environmental stimuli. Microarray technology offers the opportunity to investigate gene expression changes relevant to key processes in wastewater treatment, using A. temperans as a model organism. The aims of this research were to develop a full genome microarray platform for A. temperans CB2 and to use this microarray platform to investigate major differences in gene expression between the Hpos and Hneg phenotypic variants. An optimised gene expression microarray platform was established through the assessment of various experimental methods, such as RNA extraction, RNA amplification, microarray probe design, and quantitative PCR. Using the microarray platform, gene expression comparisons were obtained for planktonic broth cultures, static biofilms and bacterial colonies. Gene expression analyses have provided insights into the complex developmental processes involved in the transition from planktonic cells to stages of initial attachment, cell proliferation, biofilm maturation and nutrient limitation during the formation of A. temperans biofilms. Factors that have been identified in other bacterial systems such as type IV pili and activation of stress responses were also observed in A. temperans biofilms. In addition, several intriguing classes of genes, such as transcriptional regulators, a toxinantitoxin gene cassette, and nitrate metabolism were also found to be differentially expressed during the formation of A. temperans biofilm. The incorporation of microarray technology with other functional genomics techniques to investigate molecular mechanisms underlying the complex processes occurring in wastewater treatment will provide a scientific basis to improve the reliability of current wastewater treatment strategies and for the development of new treatment technologies.
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O'Neill, Paul. "Improved analysis of microarray images". Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435755.
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Selvaraja, Sudarshan. "Microarray Data Analysis Tool (MAT)". University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806.
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Stephens, Nathan W. "A comparison of genetic microarray analyses : a mixed models approach versus the significance analysis of microarrays /". Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1604.pdf.
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Zhou, Feng. "Contaminated Chi-square Modeling and Its Application in Microarray Data Analysis". UKnowledge, 2014. http://uknowledge.uky.edu/statistics_etds/7.
Testo completoAbstract (sommario):
Mixture modeling has numerous applications. One particular interest is microarray data analysis. My dissertation research is focused on the Contaminated Chi-Square (CCS) Modeling and its application in microarray. A moment-based method and two likelihood-based methods including Modified Likelihood Ratio Test (MLRT) and Expectation-Maximization (EM) Test are developed for testing the omnibus null hypothesis of no contamination of a central chi-square distribution by a non-central Chi-Square distribution. When the omnibus null hypothesis is rejected, we further developed the moment-based test and the EM test for testing an extra component to the Contaminated Chi-Square (CCS+EC) Model. The moment-based approach is easy and there is no need for re-sampling or random field theory to obtain critical values. When the statistical models are complicated such as large mixtures of dimensional distributions, MLRT and EM test may have better power than moment based approaches, and the MLRT and EM tests developed herein enjoy an elegant asymptotic theory.
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Fujita, André. "Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias". Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-14092007-173758/.
Testo completoAbstract (sommario):
A análise da expressão gênica através de dados gerados em experimentos de microarrays de DNA vem possibilitando uma melhor compreensão da dinâmica e dos mecanismos envolvidos nos processos celulares ao nível molecular. O aprimoramento desta análise é crucial para o avanço do conhecimento sobre as bases moleculares das neoplasias e para a identificação de marcadores moleculares para uso em diagnóstico, desenho de novos medicamentos em terapias anti-tumorais. Este trabalho tem como objetivos o desenvolvimento de modelos de análise desses dados, propondo uma nova forma de normalização de dados provenientes de microarrays e dois modelos para a construção de redes regulatórias de expressão gênica, sendo uma baseada na conectividade dinâmica entre diversos genes ao longo do ciclo celular e a outra que resolve o problema da dimensionalidade, em que o número de experimentos de microarrays é menor que o número de genes. Apresenta-se, ainda, um pacote de ferramentas com uma interface gráfica de fácil uso contendo diversas técnicas de análise de dados já conhecidas como também as abordagens propostas neste trabalho.The analyses of DNA microarrays gene expression data are allowing a better comprehension of the dynamics and mechanisms involved in cellular processes at the molecular level. In the cancer field, the improvement of gene expression interpretation is crucial to better understand the molecular basis of the neoplasias and to identify molecular markers to be used in diagnosis and in the design of new anti-tumoral drugs. The main goals of this work were to develop a new method to normalize DNA microarray data and two models to construct gene expression regulatory networks. One method analyses the dynamic connectivity between genes through the cell cycle and the other solves the dimensionality problem in regulatory networks, meaning that the number of experiments is lower than the number of genes. We also developed a toolbox with a user-friendly interface, displaying several established statistical methods implemented to analyze gene expression data as well as the new approaches presented in this work.
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Kapur, Karen Anita. "Low-level analysis of microarray probes on exon-targeting microarrays : modeling background, gene expression and cross-hybridization /". May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Morales, Narváez Eden. "Nanomaterials based microarray platforms for biodetection". Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/286742.
Testo completoAbstract (sommario):
Analytical disciplines are an important field for the progress of healthcare and medicine. In fact the technologies related to analytical disciplines may reveal important information for early diagnosis, treatment of diseases, food safety and environmental monitoring. In this regard, novel advances in analytical disciplines are highly desired. As a promising tool, biosensors are useful systems that enable the detection of agents with diagnostic interest. Since nanotechnology enables the manipulation and control at the nanoscale, biosensors based on nanotechnology offer powerful capabilities to diagnostic technology. In this dissertation, the advantages of the integration of nanomaterials into microarray technology are widely studied, generally in terms of sensitivity. Particularly, the performance of cadmium-selenide/zinc-sulfide (CdSe@ZnS) quantum dots (QDs) and the fluorescent dye Alexa 647 as reporter in an assay designed to detect apolipoprotein E (ApoE) has been compared. The assay is a sandwich immunocomplex microarray that functions via excitation by visible light. ApoE was chosen for its potential as a biomarker for Alzheimer's disease. The two versions of the microarray (QD or Alexa 647) were assessed under the same experimental conditions. The QDs proved to be highly e¿ective reporters in the microarrays, although their performance strongly varied in function of the excitation wavelength. At 633 nm, the QD microarray, at an excitation wavelength of 532 nm, provided a limit of detection (LOD) of ~62 pg mL-1, ¿ve times more sensitive than that of the Alexa microarray (~307 pg mL-1). Finally, serial dilutions from a human serum sample were assayed with high sensitivity and acceptable precision and accuracy (Anal. Chem. 2012, 84:6821).
Since graphene oxide (GO) is a recently discovered nanomaterial and microarray technology relies on optical signals, the photonic properties of GO are discussed and the state-of-the-art of GO in optical biosensing has been widely documented (Adv. Mater. 2012, 24:3298). Furthermore, GO has been studied as a highly efficient quencher of QDs, reporting a quenching efficiency of nearly 100%. Finally, such interaction between GO and QDs has been proposed as a highly sensitive transduction system for microarray-based biodetection (Carbon 2012, 50:2987). This research aims at demonstrating how the endeavour of the fusion between nanomaterials and microarray technology exhibits enormous possibilities towards biomarker screening, food safety and environmental monitoring.Las tecnologías relacionadas con el diagnóstico son un campo importante para el progreso de la medicina y el cuidado de las salud. Por ejemplo, estas tecnologías pueden aportar valiosa información para el tratamiento y diagnóstico temprano de enfermedades, seguridad en alimentos y monitoreo del medio ambiente. En este contexto, los sistemas de biosensado son una herramienta muy prometedora que permite la detección de agentes con interés diagnostico. Dado que la nanotecnología facilita la manipulación y control a la nanoescala, los sistemas de biodetección basados en nanotecnología poseen poderosas capacidades que pueden ser explotadas en las tecnologías relacionadas con el diagnóstico. En esta tesisis se han estudiado las ventajas que aporta la integración de nanomateriales a la tecnología de microarrays, generalmente en términos de sensibilidad. Particularmente, se ha estudiado el desempeño de la integración de nanocristales semiconductores (NS) para la detección de un biomarcador relacionado con Alzheimer en formato microarray. En dicho microarray se ha observado un importante rendimiento, mostrando un excelente limite de detección de 62 pg mL-1, el cual supera a otros metodos convencionales de detección como el ELISA (470 pg mL-1). También se ha analizado un banco de diluciones de una muestra de suero humano con precisión y exactitud aceptables (Anal. Chem. 2012, 84:6821). Por otra parte, ya que el óxido de grafeno (OG) es un material muy novedoso y la tecnología de microarrays depende de señales ópticas, se ha documentado ampliamente el estado del arte sobre el uso de (OG) en en el campo del biosensado óptico (Adv. Mater. 2012, 24:3298). Adicionalmente, se ha estudiado al OG como un desactivador de fluorescencia de NS altamente eficiente, presentando una eficiencia en la desactivación de NS de casi el 100%. Finalmente se ha aplicado dicha interacción entre NS y OG para diseñar un sistema de transducción altamente sensible (Carbon 2012, 50:2987 ). Esta investigación tiene por objetivo demostrar las ventajas y el potencial que posee la fusión entre los nanomateriales y la tecnología de microarrays como un sistema aplicado al campo del diagnóstico
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Fellenberg, Kurt. "Storage and analysis of microarray data". [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964718839.
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Hare, Brian K. Dinakarpandian Deendayal. "Feature selection in DNA microarray analysis". Diss., UMK access, 2004.
Cerca il testo completoAbstract (sommario):
Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2004."A thesis in computer science." Typescript. Advisor: D. Dinakarpandian. Vita. Title from "catalog record" of the print edition Description based on contents viewed Feb. 24, 2006. Includes bibliographical references (leaves 81-86 ). Online version of the print edition.
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Hultin, Emilie. "Genetic Sequence Analysis by Microarray Technology". Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4330.
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Lindroos, Katarina. "Accessing Genetic Variation by Microarray Technology". Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5251-5/.
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Liljedahl, Ulrika. "Microarray Technology for Genotyping in Pharmacogenetics". Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4222.
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Zhou, Ye. "Microcontact printing for protein microarray applications /". Linköping : Univ, 2004. http://www.bibl.liu.se/liupubl/disp/disp2004/tek886s.pdf.
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Eijsden, Rudy Gerardus Elisabeth van. "Microarray analysis of oxidative phosphorylation disorders". [Maastricht] : Maastricht : Maastricht University ; University Library, Universiteit Maastricht [host], 2008. http://arno.unimaas.nl/show.cgi?fid=10708.
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Peeters, Justine Kate. "Microarray bioinformatics and applications in oncology". [S.l.] : Rotterdam : [The Author] ; Erasmus University [Host], 2008. http://hdl.handle.net/1765/12618.
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Ronksley, Jonathan N. "Microarray Analysis of P19CL6 Cardiac Differentiation". Thesis, University of Nottingham, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518881.
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Huang, Liping. "STATISTICAL METHODS IN MICROARRAY DATA ANALYSIS". UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_diss/795.
Testo completoAbstract (sommario):
This dissertation includes three topics. First topic: Regularized estimation in the AFT model with high dimensional covariates. Second topic: A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data. Third topic: Normalization and analysis of cDNA microarray using linear contrasts.
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Lau, Kin-chong, e 劉健莊. "Microarray-based investigations of genetic diseases". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45894760.
Testo completo
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Hsu, Jessie. "Outcome-Driven Clustering of Microarray Data". Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10410.
Testo completoAbstract (sommario):
The rapid technological development of high-throughput genomics has given rise to complex high-dimensional microarray datasets. One strategy for reducing the dimensionality of microarray experiments is to carry out a cluster analysis to find groups of genes with similar expression patterns. Though cluster analysis has been studied extensively, the clinical context in which the analysis is performed is usually considered separately if at all. However, allowing clinical outcomes to inform the clustering of microarray data has the potential to identify gene clusters that are more useful for describing the clinical course of disease. The aim of this dissertation is to utilize outcome information to drive the clustering of gene expression data. In Chapter 1, we propose a joint clustering model that assumes a relationship between gene clusters and a continuous patient outcome. Gene expression is modeled using cluster specific random effects such that genes in the same cluster are correlated. A linear combination of these random effects is then used to describe the continuous clinical outcome. We implement a Markov chain Monte Carlo algorithm to iteratively sample the unknown parameters and determine the cluster pattern. Chapter 2 extends this model to binary and failure time outcomes. Our strategy is to augment the data with a latent continuous representation of the outcome and specify that the risk of the event depends on the latent variable. Once the latent variable is sampled, we relate it to gene expression via cluster specific random effects and apply the methods developed in Chapter 1. The setting of clustering longitudinal microarrays using binary and survival outcomes is considered in Chapter 3. We propose a model that incorporates a random intercept and slope to describe the gene expression time trajectory. As before, a continuous latent variable that is linearly related to the random effects is introduced into the model and a Markov chain Monte Carlo algorithm is used for sampling. These methods are applied to microarray data from trauma patients in the Inflammation and Host Response to Injury research project. The resulting partitions are visualized using heat maps that depict the frequency with which genes cluster together.
32
Brandt, Regine, Robert Grützmann, Andrea Bauer, Ralf Jesenofsky, Jörg Ringel, Matthias Löhr, Christian Pilarsky e Jörg D. Hoheisel. "DNA microarray analysis of pancreatic malignancies". Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-136556.
Testo completoAbstract (sommario):
Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. To improve the prognosis, novel molecular markers and targets for earlier diagnosis and adjuvant and/or neoadjuvant treatment are needed. Recent advances in human genome research and high-throughput molecular technologies make it possible to cope with the molecular complexity of malignant tumors. With DNA array technology, mRNA expression levels of thousand of genes can be measured simultaneously in a single assay. As several studies using microarrays in PDAC have already been published, this review attempts to compare the published data and therefore to validate the results. In addition, the applied techniques are discussed in the context of pancreatic malignanciesDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich
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Jen, Chih-Hung. "Microarray data analysis for Arabidopsis thaliana". Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417754.
Testo completo
34
Rogers, Simon David. "Machine learning techniques for microarray analysis". Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409426.
Testo completo
35
Pearson, Richard Peter. "Developing and benchmarking microarray analysis methods". Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549322.
Testo completo
36
Caygill, J. S. "Microarray sensors for detecting airborne explosives". Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/9174.
Testo completoAbstract (sommario):
Due to the enhanced level of national security currently required due to the possibility of terrorist attack, monitoring devices for trace levels of explosive materials are now of the upmost importance. One such method that offers a possible route towards the development of a system for the detection of such analytes is via an electrochemical regime, coupled to the use of disposable sensor technology. Within this study, the use of modified carbon screen-printed sensors for the detection and analysis of such analytes of importance has been investigated. The modification of the base carbon substrate has been undertaken in a two-fold manner; firstly the incorporation of an enhanced electroactive mediator (Cobalt Phthalocyanine) has been investigated as an aid to facilitate the signal response and secondly the use of a novel surface modification technique to produce microelectrode arrays upon the carbon has also been employed. Microelectrodes hold intrinsic advantages over planar electrodes, such as stir independence, low detection limits and increased sensitivity due to their hemispherical diffusional profile. An array of microelectrodes can retain these properties whilst including the added advantage of enhancing the current response. The integration of these two approaches, the microelectrode array coupled with the mediated electrodes, has been developed with the ultimate objective to develop an accurate and sensitive detection system for trace quantities of explosives, namely 2,4,6-trinitrotoluene (TNT). This thesis describes work focussed towards the optimisation of each of the individual components involved in the formation of a sensing device for the detection and measurement of trace levels of explosive materials. In particular, factors and techniques that may facilitate the enhanced sensitivity of the measurement device are described. At every stage, each modification step was also undertaken with a suitable redox probe, ferrocenemonocarboxylic acid to allow for a quantitative assessment to be made. The use of unmediated and mediated carbon ink has been assessed in terms of suitability as a host material for the detection of TNT, with concentrations of 400 nM being measured on these base substrates. Further to this, microelectrode arrays were then formed upon these planar carbon surfaces via insulation with poly(phenylenediamine) coating and subsequent ultrasonic ablation. These thin film microelectrode arrays (~40 nm, pore population ~7.0 x 104 cm- 2 ) were also investigated in terms of response to TNT and were seen to offer an enhanced response in terms of signal differentiation. A final stage was then applied where the microelectrode array was further modified to incorporate a conductively grown polymer from the pore areas. Within this conductive growth, an enzyme/co-factor matrix specific to TNT was deposited which was seen to further increase signal responses, although displaying a lack of sensitivity at lower concentrations. As a final step the developed sensor methodologies were then used in conjunction with an airsampling system, the Coriolis®µ cyclone, to mimic the use of the sensors in realistic environments for practical employment. The sensors were used to successfully measure TNT samples from a concentrated stock sample of 4.4 mM collected via the cyclone technique.
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Khodiyar, Varsha Kumari. "Microarray profiling of inflammatory bowel disease". Thesis, University of Leicester, 2002. http://hdl.handle.net/2381/29415.
Testo completoAbstract (sommario):
In this study of inflammatory bowel diseases (IBD, i.e. Crohn's disease and ulcerative colitis), the gene transcription profile of colonic IBD resection specimens were analysed by oligonucleotide microarray analysis. A total of 33,625 genes were profiled across 23 colonic mucosa samples; 5 involved Crohn's disease, 4 uninvolved Crohn's disease, 5 involved ulcerative colitis, 3 uninvolved ulcerative colitis and 6 samples from macroscopically normal areas of colorectal cancer resections (controls). A number of data-mining tools, encompassing clustering (e.g. hierarchical & K-means) and matrix-based methods were evaluated for the analysis of this microarray data. Mining strategies were formulated, tested and then applied to the data set to identify genes showing interesting and novel expression patterns across the samples. The application of these tools to the data set resulted in the generation of gene expression profiles for Crohn's disease and ulcerative colitis. Genes of interest were annotated using publicly accessible sequence and literature databases. Potential links by previous research in the inflammatory bowel disease field were analysed for selected genes. Common pathways emerging from the annotation effort and potentially linking together several of the genes of interest were investigated. Specifically, the energy deficiency hypothesis proposed by Roediger in 1980 and the relevance of potential cancer and apoptosis related genes were reviewed with regard to the findings.
38
Amaral, Telmo. "Analysis of breast tissue microarray spots". Thesis, University of Dundee, 2010. https://discovery.dundee.ac.uk/en/studentTheses/0a83915d-2f11-4b89-9c24-8dc3c15346f2.
Testo completoAbstract (sommario):
Tissue microarrays (TMAs) are a high-throughput technique that facilitates the survey of very large numbers of tumours, important both in clinical and research applications. However, the assessment of stained TMA sections is laborious and still needs to be carried manually, constituting a bottleneck in the pathologist?s work-flow. This process is also prone to perceptual errors and observer variability.Thus, there is strong motivation for the development of automated quantitative analysis of TMA image data. The analysis of breast TMA sections subjected to nuclear immunostaining begins with the classification of each spot as to the maintype of tissue that it contains, namely tumour, normal, stroma, or fat. Tumour and normal spots are then assigned a so-called quickscore composed of a pair or integer values, the first reflecting the proportion of epithelial nuclei that are stained, and the second reflecting the strength of staining of those nuclei. In this work, an approach was developed to analyse breast TMA spots subjectedto progesterone receptor immunohistochemistry. Spots were classified into their four main types through a method that combined a bag of features approachand classifiers based on either multi-layer perceptrons or latent Dirichlet allocation models. A classification accuracy of 74.6 % was achieved. Tumour and normal spots were scored via an approach that involved the computation of global features formalising the quickscore values used by pathologists, and the use of Gaussian processes for ordinal regression to predict actual quickscores based on global features. Mean absolute errors of 0.888 and 0.779 were achieved in the prediction of the first and second quickscore values, respectively. By setting thresholds on prediction confidence, it was possible to classify and score fractions of spots with substantially higher accuracies and lower mean absolute errors. Amethod for the segmentation of TMA spots into regions of different types was also investigated, to explore the generative nature of latent Dirichlet allocation models.
39
Brandt, Regine, Robert Grützmann, Andrea Bauer, Ralf Jesenofsky, Jörg Ringel, Matthias Löhr, Christian Pilarsky e Jörg D. Hoheisel. "DNA microarray analysis of pancreatic malignancies". Karger, 2004. https://tud.qucosa.de/id/qucosa%3A27711.
Testo completoAbstract (sommario):
Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. To improve the prognosis, novel molecular markers and targets for earlier diagnosis and adjuvant and/or neoadjuvant treatment are needed. Recent advances in human genome research and high-throughput molecular technologies make it possible to cope with the molecular complexity of malignant tumors. With DNA array technology, mRNA expression levels of thousand of genes can be measured simultaneously in a single assay. As several studies using microarrays in PDAC have already been published, this review attempts to compare the published data and therefore to validate the results. In addition, the applied techniques are discussed in the context of pancreatic malignancies.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
40
Bertone, Paul. "Microarray Approaches to Experimental Genome Annotation". Diss., Yale University, 2005. http://hdl.handle.net/10919/71577.
Testo completoAbstract (sommario):
This work describes the development and application of genomic DNA tiling arrays: microarrays designed to represent all of the DNA comprising a chromosome or other genomic locus, regardless of the genes that may be annotated in the region of interest. Because tiling arrays are intended for the unbiased interrogation of genomic sequence, they enable the discovery of novel functional elements beyond those described by existing gene annotation. This is of particular importance in mapping the gene structures of higher eukaryotes, where combinatorial exon usage produces rare splice variants or isoforms expressed in low abundance that may otherwise elude detection. Issues related to the design of both oligonucleotide- and amplicon-based tiling arrays are discussed; the latter technology presents distinct challenges related to the selection of suitable amplification targets from genomic DNA. Given the widespread fragmentation of mammalian genomes by repetitive elements, obtaining maximal coverage of the non-repetitive sequence with a set of fragments amenable to high-throughput polymerase chain reaction (PCR) amplification represents a non-trivial optimization problem. To address this issue, several algorithms are described for the efficient computation of optimal tile paths for the design of amplicon tiling arrays. Using these methods, it is possible to recover an optimal tile path that maximizes the coverage of non-repetitive DNA while minimizing the number of repetitive elements included in the resulting sequence fragments. Tiling arrays were constructed and used for the chromosome- and genome-wide assessment of human transcriptional activity, via hybridization to complementary DNA derived from polyadenylated RNA expressed in normal complex tissues. The approach is first demonstrated with amplicon arrays representing all of the non-repetitive DNA of human chromosome 22, then extended to the entire genome using maskless photolithographic DNA synthesis technology. A large-scale tiling array survey revealed the presence of over 10,000 novel transcribed regions and verified the expression of nearly 13,000 predicted genes, providing the first global transcription map of the human genome. In addition to those likely to encode protein sequences on the basis of evolutionary sequence conservation, many of the novel transcripts constitute a previously uncharacterized population of non-coding RNAs implicated in myriad structural, catalytic and regulatory functions.
41
Teixeira, Bellina Ribau. "Computational methods for microarray data analysis". Master's thesis, Universidade de Aveiro, 2009. http://hdl.handle.net/10773/3989.
Testo completoAbstract (sommario):
Mestrado em Engenharia de Computadores e TelemáticaOs microarrays de ácido desoxirribonucleico (ADN) são uma importante tecnologia para a análise de expressão genética. Permitem medir o nível de expressão de genes em várias amostras para, por exemplo, identificar genes cuja expressão varia com a administração de determinado medicamento. Um slide de microarray mede o nível de expressão de milhares de genes numa amostra ao mesmo tempo e uma experiência pode usar vários slides, surgindo assim muitos dados que é preciso processar e analisar, com recurso a meios informáticos. Esta dissertação inclui um levantamento de métodos e recursos de software utilizados na análise de dados de experiências de microarrays. Em seguida, descreve-se o desenvolvimento de um novo módulo de análise de dados que visa, usando métodos de identificação de genes diferencialmente expressos, identificar genes que se encontram diferencialmente expressos entre dois ou mais grupos experimentais. No final, é apresentado o trabalho resultante, a nível de interfaces gráficas e funcionamento.
Deoxyribonucleic acid (DNA) microarrays are an important technology for the analysis of gene expression. They allow measuring the expression of genes among several samples in order to, for example, identify genes whose expression varies with the administration of a certain drug. A microarray slide measures the expression level of thousands of genes in a sample at the same time, and an experiment can include various slides, leading to a lot of data to be processed and analyzed, with the aid of computerized means. This dissertation includes a review of methods and software tools used in the analysis of microarray experimental data. Then it is described the development of a new data analysis module that intends, using methods of identifying differentially expressed genes, to identify genes that are differentially expressed between two more groups. Finally, the resulting work is presented, describing its graphical interface and structural design.
42
Sehlstedt, Jonas. "Replacing qpcr non-detects with microarray expression data : An initialized approach towards microarray and qPCR data integration". Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15790.
Testo completoAbstract (sommario):
Gene expression analysis can be performed by a number of methods. One of the most common methods is using relative qPCR to assess the relative expression of a determined set of genes compared to a reference gene. Analysis methods benefits from an as homogeneous sample set as possible, as great variety in original sample disease status, quality, type, or distribution may yield an uneven base expression between replicates. Additionally normalization of qPCR data will not work if there are missing values in the data. There are methods for handling non-detects (i.e. missing values) in the data, where most of them are only recommended to use when there is a single, or very few, value missing. By integrating microarray expression data with qPCR data, the data quality could be improved on, eradicating the need to redo an entire experiment when too much data is missing or sample data too is heterogeneous. In this project, publically available microarray data, with similar sample status of a given qPCR dataset, was downloaded and processed. The qPCR dataset included 51 genes, where a set of four DLG genes has been chosen for in-depth analysis. For handling missing values, mean imputation and inserting Cq value 40 were used, as well as a novel method initialized where microarray data was used to replace missing values. In summary replacing missing values with microarray data did not show any significant difference to the other two methods in three of the four DLG genes. From this project, it is also suggested an initialized approach towards testing the possibility of qPCR and microarray data integration.
43
Szeto, Lap Keung. "Clustering analysis of microarray gene expression data /". access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-it-b19885817a.pdf.
Testo completoAbstract (sommario):
Thesis (M.Phil.)--City University of Hong Kong, 2005."Submitted to Department of Computer Engineering and Information Technology in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 70-79)
44
Botella, Pérez Cristina. "Multivariate classification of gene expression microarray data". Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/9046.
Testo completoAbstract (sommario):
L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal.
45
Lee, Kyeong Eun. "Bayesian models for DNA microarray data analysis". Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.
Testo completoAbstract (sommario):
Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.
46
Lönnstedt, Ingrid. "Empirical Bayes Methods for DNA Microarray Data". Doctoral thesis, Uppsala University, Department of Mathematics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5865.
Testo completoAbstract (sommario):
cDNA microarrays is one of the first high-throughput gene expression technologies that has emerged within molecular biology for the purpose of functional genomics. cDNA microarrays compare the gene expression levels between cell samples, for thousands of genes simultaneously.
The microarray technology offers new challenges when it comes to data analysis, since the thousands of genes are examined in parallel, but with very few replicates, yielding noisy estimation of gene effects and variances. Although careful image analyses and normalisation of the data is applied, traditional methods for inference like the Student t or Fisher’s F-statistic fail to work.
In this thesis, four papers on the topics of empirical Bayes and full Bayesian methods for two-channel microarray data (as e.g. cDNA) are presented. These contribute to proving that empirical Bayes methods are useful to overcome the specific data problems. The sample distributions of all the genes involved in a microarray experiment are summarized into prior distributions and improves the inference of each single gene.
The first part of the thesis includes biological and statistical background of cDNA microarrays, with an overview of the different steps of two-channel microarray analysis, including experimental design, image analysis, normalisation, cluster analysis, discrimination and hypothesis testing. The second part of the thesis consists of the four papers. Paper I presents the empirical Bayes statistic B, which corresponds to a t-statistic. Paper II is based on a version of B that is extended for linear model effects. Paper III assesses the performance of empirical Bayes models by comparisons with full Bayes methods. Paper IV provides extensions of B to what corresponds to F-statistics.
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Kocabas, Fahri. "Knowledge Discovery In Microarray Data Of Bioinformatics". Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615090/index.pdf.
Testo completoAbstract (sommario):
This thesis analyzes major microarray repositories and presents a metadata
framework both to address the current issues and to promote the main operations
such as knowledge discovery, sharing, integration, and exchange. The proposed
framework is demonstrated in a case study on real data and can be used for other
high throughput repositories in biomedical domain.
Not only the number of microarray experimentation increases, but also the
size and complexity of the results rise in response to biomedical inquiries. And,
experiment results are significant when examined in a batch and placed in a
biological context. There have been standardization initiatives on content, object
model, exchange format, and ontology. However, they have proprietary information
space. There are backlogs and the data cannot be exchanged among the repositories.
There is a need for a format and data management standard at present.iv
v
We introduced a metadata framework to include metadata card and semantic
nets to make the experiment results visible, understandable and usable. They are
encoded in standard syntax encoding schemes and represented in XML/RDF. They
can be integrated with other metadata cards, semantic nets and can be queried. They
can be exchanged and shared. We demonstrated the performance and potential
benefits with a case study on a microarray repository.
This study does not replace any product on repositories. A metadata
framework is required to manage such huge data. We state that the backlogs can be
reduced, complex knowledge discovery queries and exchange of information can
become possible with this metadata framework.
48
Li, Ying. "Efficient Combinatorial Algorithms for DNA Microarray Design". Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490907.
Testo completoAbstract (sommario):
The advent of efficient genome sequencing tools and high-throughput experimental biotechnology has led to enonnous progress in the life science. DNA microarray is among the most important innovations. It allows to measure the expression for thousands of genes simultaneously by analysing the hybridisation data. Such measurements have been proved to be invaluable in understanding the development of diseases such as cancer. However, the analysis of data is non-trivial since the hybridisation data relies on the quality of DNA microarray. High quality DNA microarray will lead to more efficient hybridisation and stronger signal.and reliability. The reliability of data is essential. Thus, the development of novel algorithms and techniques for DNA microarray design is crucial. This thesis considers a number of combinatorial issues in selecting, placing, and synthesising probes during the DNA microarray design process. A probe is a specific sequence of single-stranded DNA or RNA, typically labelled with a radioactive or fluorescent tag, which is designed to bind to, and thereby identify, a particular segment of DNA (or RNA). The probe selection problem we studied is to find for each gene sequence a unique probe such that every gene in the given dataset can be identified. However, due to homology, sometimes a gene does not have a unique probe, then we use a small number of non-unique probes to identify a gene. The challenge of the problem is that there are many candidate probes in a gene sequence and we have to find the right one (or a small subset) efficiently. A randomised probe selection algorithm for DNA microarray design is proposed. The algorithm overcomes some existing algorithms demanding optimal probes by exhaustive search. \Ve implement the randomised probe selection algorithm and develop a probe selection software RANDPS. Investigations using several real-life microarray datasets show that algorithm is able to find high quality probes. Nevertheless, the number of the probes selected might be too large for placing in a single microarray, thus minimising the number of probes is an important objective, since it is proportional to the cost of the microarray experiment. Therefore, we investigate the string barcoding problem in which a set of non-unique probes is given and the probes have to be chosen from the given set of probes. The objective is to use an appropriate combination of probes with minimum cardinality such that all genes in the dataset can be distinguished. An almost optimal O(nlSllog3 n)-time approximation algorithm for the considered problem is presented. The approximation procedure is a modification of the algorithm due to Berman et a1. [l0] which obtains the best possible approximation ratio (1 + In n). The improved time complexity is a direct consequence of more careful management of processed sets, use of several specialised graph and string data structures, as well as tighter time complexity analysis based on an amortised argument. After probes are selected, they are then synthesised on the microarrays by using a light-directed chemical process in which unintended illumination may contaminate the quality of the microarray experiments. Border length is a measure of the amount of unwanted illumination and the objective of this problem is to minimise the total border length during probe synthesis process. This problem is believed to be NP-hard and approximation of the BMP problem in asynchronous synthesis is studied. As far as we know, this is the first result with proved performance guarantee. The main result is an O(vnlog2 n)-approximation, where n is the number of probes to be synthesised. In the case where the placement is given in advance, we show that the problem is O(10g2 n)-approximable. A related problem called agreement maximisation problem (MAP) is also considered in this chapter. In contrast to BMp, we show that MAP admits a constant approximation even when placement is not given in advance. Supplied by The British Library - 'The world's knowledge'
49
Moertel, Luke Paul Frank, e mobileluke@hotmail com /. Luke Moertel@qimr edu au. "Microarray Analysis of the Schistosoma japonicum Transcriptome". Central Queensland University. Chemical and Biomedical Sciences, 2007. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20070705.120939.
Testo completoAbstract (sommario):
Schistosomiasis, a disease of humans caused by helminth parasites of the genus Schistosoma, kills 200 to 500 thousand people annually, endangering over 600 million people world-wide with 200 million people infected in 2003 [1, 2]. Three species of schistosome are primarily responsible for human infections, namely, Schistosoma haematobium, endemic to Africa, India, and the Middle East, S. mansoni, endemic to Africa / South America, and S. japonicum endemic to China and the Philippines [3]. The major pathological effects of schistosomiasis result from the deposition of parasite ova in human tissues and the subsequent intense granulomatous response induced by these eggs. There is a high priority to provide an effective sub-unit vaccine against these schistosome flukes, using proteins encoded by cDNAs expressed by the parasites at critical phases of their development. One technique that may expedite this gene identification is the use of microarrays for expression analysis. A 22,575 feature custom oligonucleotide DNA microarray designed from public domain databases of schistosome ESTs (Expressed Sequence Tags) was used to explore differential gene expression between the Philippine (SJP) and Chinese (SJC) strains of S. japonicum, and between males and females. It was found that 593, 664 and 426 probes were differentially expressed between the two geographical strains when mix sexed adults, male worms and female worms were compared respectively. Additionally, the study revealed that 1,163 male- and 1,016 female-associated probes were differentially expressed in SJP whereas 1,047 male- and 897 female-associated probes were differentially expressed in SJC [4]. Further to this, a detailed real time PCR expression study was used to explore the differential expression of eight genes of interest throughout the SJC life cycle, which showed that several of the genes were down-regulated in different life cycle stages. The study has greatly expanded previously published data of strain and gender-associated differential expression in S. japonicum. Further, the new data will provide a stepping stone for understanding the complexities of the biology, sexual differentiation, maturation, and development of human schistosomes, signaling new approaches for identifying novel intervention and diagnostic targets against schistosomiasis [4].
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Eijssen, Lars Maria Theo. "Analysis of microarray gene expression data sets". [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Universiteit Maastricht [host], 2006. http://arno.unimaas.nl/show.cgi?fid=6830.
Testo completo