Dissertations / Theses on the topic 'Microarray'
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Pernagallo, Salvatore. "Biocompatible polymer microarrays for cellular high-content screening." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/7571.
Full textWang, Tao. "Statistical design and analysis of microarray experiments." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.
Full textTitle 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
Klenkar, Goran. "Protein Microarray Chips." Doctoral thesis, Linköping : Univ, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8904.
Full textHarness, 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.
Full textDvergsten, Erik C. "A Weighted Gene Co-expression Network Analysis for Streptococcus sanguinis Microarray Experiments." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4430.
Full textHernández-Cabronero, Miguel. "DNA Microarray Image Compression." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/297706.
Full textIn 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.
Downes, Aidan Rawle. "Microarray submissions to Experibase." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33281.
Full textIncludes 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.
Mao, Shihong. "Comparative Microarray Data Mining." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.
Full textStephens, 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.
Full textFronczyk, 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.
Full textNowacki, Sandra. "DPC4-Zielgenidentifikation mittels Microarray-Technologie." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975910027.
Full textLau, Kelvin Ee Ming. "Microarray analysis of Acidovorax temperans." Thesis, University of Auckland, 2008. http://hdl.handle.net/2292/5869.
Full textO'Neill, Paul. "Improved analysis of microarray images." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435755.
Full textSelvaraja, Sudarshan. "Microarray Data Analysis Tool (MAT)." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806.
Full textStephens, 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.
Full textZhou, Feng. "Contaminated Chi-square Modeling and Its Application in Microarray Data Analysis." UKnowledge, 2014. http://uknowledge.uky.edu/statistics_etds/7.
Full textFujita, 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/.
Full textThe 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.
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.
Full textMorales, Narváez Eden. "Nanomaterials based microarray platforms for biodetection." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/286742.
Full textLas 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
Fellenberg, Kurt. "Storage and analysis of microarray data." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964718839.
Full textHare, Brian K. Dinakarpandian Deendayal. "Feature selection in DNA microarray analysis." Diss., UMK access, 2004.
Find full text"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.
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.
Full textLindroos, 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/.
Full textLiljedahl, 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.
Full textZhou, Ye. "Microcontact printing for protein microarray applications /." Linköping : Univ, 2004. http://www.bibl.liu.se/liupubl/disp/disp2004/tek886s.pdf.
Full textEijsden, 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.
Full textPeeters, Justine Kate. "Microarray bioinformatics and applications in oncology." [S.l.] : Rotterdam : [The Author] ; Erasmus University [Host], 2008. http://hdl.handle.net/1765/12618.
Full textRonksley, Jonathan N. "Microarray Analysis of P19CL6 Cardiac Differentiation." Thesis, University of Nottingham, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518881.
Full textHuang, Liping. "STATISTICAL METHODS IN MICROARRAY DATA ANALYSIS." UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_diss/795.
Full textLau, Kin-chong, and 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.
Full textHsu, Jessie. "Outcome-Driven Clustering of Microarray Data." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10410.
Full textBrandt, Regine, Robert Grützmann, Andrea Bauer, Ralf Jesenofsky, Jörg Ringel, Matthias Löhr, Christian Pilarsky, and 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.
Full textDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich
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.
Full textRogers, Simon David. "Machine learning techniques for microarray analysis." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409426.
Full textPearson, Richard Peter. "Developing and benchmarking microarray analysis methods." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549322.
Full textCaygill, J. S. "Microarray sensors for detecting airborne explosives." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/9174.
Full textKhodiyar, Varsha Kumari. "Microarray profiling of inflammatory bowel disease." Thesis, University of Leicester, 2002. http://hdl.handle.net/2381/29415.
Full textAmaral, Telmo. "Analysis of breast tissue microarray spots." Thesis, University of Dundee, 2010. https://discovery.dundee.ac.uk/en/studentTheses/0a83915d-2f11-4b89-9c24-8dc3c15346f2.
Full textBrandt, Regine, Robert Grützmann, Andrea Bauer, Ralf Jesenofsky, Jörg Ringel, Matthias Löhr, Christian Pilarsky, and Jörg D. Hoheisel. "DNA microarray analysis of pancreatic malignancies." Karger, 2004. https://tud.qucosa.de/id/qucosa%3A27711.
Full textDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
Bertone, Paul. "Microarray Approaches to Experimental Genome Annotation." Diss., Yale University, 2005. http://hdl.handle.net/10919/71577.
Full textTeixeira, Bellina Ribau. "Computational methods for microarray data analysis." Master's thesis, Universidade de Aveiro, 2009. http://hdl.handle.net/10773/3989.
Full textOs 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.
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.
Full textSzeto, 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.
Full text"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)
Botella, Pérez Cristina. "Multivariate classification of gene expression microarray data." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/9046.
Full textLee, Kyeong Eun. "Bayesian models for DNA microarray data analysis." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.
Full textLönnstedt, 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.
Full textcDNA 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.
Kocabas, Fahri. "Knowledge Discovery In Microarray Data Of Bioinformatics." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615090/index.pdf.
Full textLi, Ying. "Efficient Combinatorial Algorithms for DNA Microarray Design." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490907.
Full textMoertel, Luke Paul Frank, and 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.
Full textEijssen, 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.
Full text