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Hernández-Cabronero, Miguel. "DNA Microarray Image Compression." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/297706.
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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.<br>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.
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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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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.
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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.
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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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Hare, Brian K. Dinakarpandian Deendayal. "Feature selection in DNA microarray analysis." Diss., UMK access, 2004.
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Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2004.<br>"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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Brandt, Regine, Robert Grützmann, Andrea Bauer, et al. "DNA microarray analysis of pancreatic malignancies." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-136556.
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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<br>Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich
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Brandt, Regine, Robert Grützmann, Andrea Bauer, et al. "DNA microarray analysis of pancreatic malignancies." Karger, 2004. https://tud.qucosa.de/id/qucosa%3A27711.
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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.<br>Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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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.
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<p>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. </p><p>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 <i>t</i> or Fisher’s <i>F</i>-statistic fail to work.</p><p>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.</p><p>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 <i>B</i>, which corresponds to a <i>t</i>-statistic. Paper II is based on a version of <i>B</i> 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 <i>B</i> to what corresponds to <i>F</i>-statistics.</p>
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Lee, Kyeong Eun. "Bayesian models for DNA microarray data analysis." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.
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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.
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Lönnstedt, Ingrid. "Empirical Bayes methods for DNA microarray data /." Uppsala : Matematiska institutionen, Univ. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5865.
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Egeland, Ryan. "An electrochemical system for DNA microarray fabrication." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400572.
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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.
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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'
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Wang, Tao. "Statistical design and analysis of microarray experiments." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.
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Thesis (Ph. D.)--Ohio State University, 2005.<br>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
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Model, Fabian. "Statistical analysis of microarray based DNA methylation data." [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1612.
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Hou, Ting. "DNA microarray-based detection of water-borne viruses." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/2165/.
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Viruses can be transmitted through and contaminate waters causing waterborne epidemics to humans and animals. There is insufficient understanding about how viruses survive in the environment, and to what extent this may differ between agents or in the co-presence of other micro-organisms. The limited data makes it difficult to determine the risks of viruses and this hinders the preparation of preventative plans against viral transmission through the waters. This project sought to establish a DNA microarraybased approach to detect and differentiate between viruses in environmental waters, to provide a sensitive, specific and rapid system for monitoring virus contamination. Such a system might provide data not only for improved predictions of the outbreak of diseases but may lead to the effective modelling ofvirus re-circulation through the environment. The Picornaviridae virus family was the focus of this project. 152 specific microarray probes were designed after using 'ClustalX' software to multiply align the respective virus sequences and conducting 'BLASTN' similarity searches to estimate their specificity. Standard and multiplex RT-PCR amplification of viral nucleic acids with direct incorporation of fluorescent Cy-dyes was combined with the DNA microarray hybridization technique to identify the virus composition of test and environmental samples. The microarray data was normalised and ranked using a range of statistical methods. After the development of appropriate detection criteria using pilot studies with known input virus samples the experimental and statistical process was applied to detection and identification of viruses within environmental samples. Following tests on a range of different techniques for RNA extraction, amplification and labelling the following optimal procedure was adopted: following the concentration of virus particles by acetone precipitation, RNA from the environmental samples was extracted using the 'QIAamp Viral RNA Mini Kit'; following olio-dT-primed cDNA synthesis, the 'Genomiphi V2 DNA Amplification Kit' was used to randomly amplify the cDNA; the DNA was then directly labelled by incorporating Cy-dyes in a PCR reaction with multiple virus-specific primers. A sewage sample was provided by the Reading HPA Environmental Virology Unit for testing in this project; they had identified a number of viruses in this sample by cell culture: Coxsackieviruses B2, B3, B4 and B5 and also detected some unknown isolates. The optimised microarray-based method developed in this project predicted the presence of the following viruses in the same sewage sample: Coxsackieviruses B4 and B3, Bovine Enterovirus, Poliovirus and Hepatitis A virus. Thtis, while some ofthe same viruses were detected by the microarray, a range of other viruses were also detected, using relatively stringent statistical thresholds. The microarray-based detection system appears to have broader specificity, and possibly sensitivity, than the cell culture-based approaches and importantly, is potentially able to direct non-cultivable and non-viable viruses in a water sample. These findings, coupled with the rapid nature of the technique, suggest that micorarrays, could, in the future, provide a superior alternative to cell culture-based methods for detection ofwaterborne viruses.
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Berrar, Daniel. "Machine learning methods for analyzing DNA microarray data." Thesis, University of Ulster, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414098.
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Loo, Lit-Hsin Kam Moshe. "Identifying differentially expressed genes in DNA microarray data /." Philadelphia, Pa. : Drexel University, 2004. http://dspace.library.drexel.edu/handle/1860/375.
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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.
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Thesis (M.Phil.)--City University of Hong Kong, 2005.<br>"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)
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Lehr, Hans-Peter. "Entwicklung eines fluoreszenz-optischen Evaneszenzfeldmesssystems für die Echtzeitanalyse von DNA-Mikroarrays." [S.l. : s.n.], 2002. http://www.freidok.uni-freiburg.de/volltexte/598.
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Benoit, Vincent. "Flow-through microchannel DNA chips." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368731.
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Qureshi, Aaron M. "A combinatorial design of a protein-binding DNA microarray." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2081.
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Thesis (M.S.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Schmitt, William A. (William Anthony) 1976. "Extracting transcriptional regulatory information from DNA microarray expression data." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28290.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2003.<br>Includes bibliographical references.<br>(cont.) As a model system, we have chosen the unicellular, photoautotrophic cyanobacteria Synechocystis sp. PCC6803 for study, as it is 1) fully sequenced, 2) has an easily manipulated input signal (light for photosynthesis), and 3) fixes carbon dioxide into the commercially interesting, biodegradable polymer polyhydroxyalkanoate (PHA). We have created DNA microarrays with [approximately]97% of the Synechocystis genome represented in duplicate to monitor the cellular transcriptional profile. These arrays are used in time-series experiments of differing light levels to measure dynamic transcriptional response to changing environmental conditions. We have developed networks of potential genetic regulatory interactions through time-series analysis based on the data from our studies. An algorithm for combining gene position information, clustering, and time-lagged correlations has been created to generate networks of hypothetical biological links. Analysis of these networks indicates that good correlation exists between the input signal and certain groups of photosynthesis- and metabolism-related genes. Furthermore, this analysis technique placed these in a temporal context, showing the sequence of potential effects from changes in the experimental conditions. This data and hypothetical interaction networks have been used to construct AutoRegressive with eXogenous input (ARX) models. These provide dynamic, state-space models for prediction of transcriptional profiles given a dynamically changing set of environmental perturbations...<br>Recent technological developments allow all the genes of a species to be monitored simultaneously at the transcriptional level. This necessitates a more global approach to biology that includes consideration of complex interactions between many genes and other intracellular species. The metaphor of a cell as a miniature chemical plant with inputs, outputs, and controls gives chemical engineers a foothold in this type of analysis. Networks of interacting genes are fertile ground for the application of the methods developed by engineers for the analysis and monitoring of industrial chemical processes. The DNA microarray has been established as a tool for efficient collection of mRNA expression data for a large number of genes simultaneously. Although great strides have been made in the methodology and instrumentation of this technique, the development of computational tools needed to interpret the results have received relatively inadequate attention. Existing analyses, such a clustering techniques applied to static data from cells at many different states, provide insight into co-expression of genes and are an important basis for exploration of the cell's genetic programming. We propose that an even greater level of regulatory detail may be gained by dynamically changing experimental conditions (the input signal) and measuring the time-delayed response of the genes (the output signal). The addition of temporal information to DNA microarray experiments should suggest potential cause/effect relationships among genes with significant regulatory responses to the conditions of interest. This thesis aims to develop computational techniques to maximize the information gained from such dynamic experiments.<br>by William A. Schmitt, Jr.<br>Ph.D.
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Hernandez-Cabronero, Miguel, Ian Blanes, Armando J. Pinho, Michael W. Marcellin, and Joan Serra-Sagrista. "Progressive Lossy-to-Lossless Compression of DNA Microarray Images." IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016. http://hdl.handle.net/10150/615540.
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The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to-lossless scheme is presented to solve this problem. First, the regular structure of the RQ intervals is exploited to define a lossy-to-lossless coding algorithm called the Progressive RQ (PRQ) coder. Second, an enhanced version that prioritizes a region of interest, called the PRQ-region of interest (ROI) coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding results with better rate-distortion performance than both the RQ and PRQ coders.
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Lee, Chi-Ying. "Characterization of DNA-functionalized surfaces for microarray and biosensor applications /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/9915.
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Karanam, Suresh Kumar. "Automation of comparative genomic promoter analysis of DNA microarray datasets." Thesis, Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04062004-164658/unrestricted/karanam%5Fsuresh%5Fk%5F200312%5Fms.pdf.
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Nitschke, Heike [Verfasser], Stefan [Gutachter] Monecke, and Reinhard [Gutachter] Berner. "Genotypisierung von Streptococcus agalactiae mithilfe des DNA-Microarray : Genotyping of Streptococcus agalactiae using the DNA microarray / Heike Nitschke ; Gutachter: Stefan Monecke, Reinhard Berner." Dresden : Technische Universität Dresden, 2019. http://d-nb.info/1227196717/34.
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Jiang, Ying, and 蔣穎. "Studies of gene regulation using microarray data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29976388.
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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/.
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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.<br>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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Engelmann, Julia Cathérine. "DNA microarrays: applications and novel approaches for analysis and interpretation." kostenfrei, 2008. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2008/2974/.
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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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Kharal, Rosina. "Semidefinite Embedding for the Dimensionality Reduction of DNA Microarray Data." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2945.
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Harnessing the power of DNA microarray technology requires the existence of analysis methods that accurately interpret microarray data. Current literature abounds with algorithms meant for the investigation of microarray data. However, there is need for an efficient approach that combines different techniques of microarray data analysis and provides a viable solution to dimensionality reduction of microarray data. Reducing the high dimensionality of microarray data is one approach in striving to better understand the information contained within the data. We propose a novel approach for dimensionality reduction of microarray data that effectively combines different techniques in the study of DNA microarrays. Our method, <strong><em>KAS</em></strong> (<em>kernel alignment with semidefinite embedding</em>), aids the visualization of microarray data in two dimensions and shows improvement over existing dimensionality reduction methods such as PCA, LLE and Isomap.
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Spiegelman, Dan. "Exploring the fusion of metagenomic library and DNA microarray technologies." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98805.
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We explored the combination of metagenomic library and DNA microarray technologies into a single platform as a novel way to rapidly screen metagenomic libraries for genetic targets. In the "metagenomic microarray" system, metagenomic library clone DNA is printed on a microarray surface, and clones of interest are detected by hybridization to single-gene probes. This study represents the initial steps in the development of this technology. We constructed two 5,000-clone large-insert metagenomic libraries from two diesel-contaminated Arctic soil samples. We developed and optimized an automated fosmid purification protocol to rapidly-extract clone DNA in a high-throughput 96-well format. We then created a series of small prototype arrays to optimize various parameters of microarray printing and hybridization, to identify and resolve technical challenges, and to provide proof-of-principle of this novel application. Our results suggest that this method shows promise, but more experimentation must be done to establish the feasibility of this approach.
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Loy, Alexander. "DNA microarray technology for biodiversity inventories of sulfate reducing prokaryotes." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=969359667.
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LeButt, Helen. "The construction and use of a Francisella tularensis DNA microarray." Thesis, Open University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446274.
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Li, Xiaopeng. "Development of Oligonucleotide Microarray for High Throughput DNA Methylation Analysis." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1224605179.
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Leung, Yuk-yee. "An integrated framework for feature selection and classification in microarray data analysis." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43278632.
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Lau, 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.
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Niu, Sanjun. "A label-free, fluorescence based assay for microarray." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11229.
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DNA chip technology has drawn tremendous attention since it emerged in the mid 90 s as a method that expedites gene sequencing by over 100-fold. DNA chip, also called DNA microarray, is a combinatorial technology in which different single-stranded DNA (ssDNA) molecules of known sequences are immobilized at specific spots. The immobilized ssDNA strands are called probes. In application, the chip is exposed to a solution containing ssDNA of unknown sequence, called targets, which are labeled with fluorescent dyes. Due to specific molecular recognition among the base pairs in the DNA, the binding or hybridization occurs only when the probe and target sequences are complementary. The nucleotide sequence of the target is determined by imaging the fluorescence from the spots.
The uncertainty of background in signal detection and statistical error in data analysis, primarily due to the error in the DNA amplification process and statistical distribution of the tags in the target DNA, have become the fundamental barriers in bringing the technology into application for clinical diagnostics. Furthermore, the dye and tagging process are expensive, making the cost of DNA chips inhibitive for clinical testing. These limitations and challenges make it difficult to implement DNA chip methods as a diagnostic tool in a pathology laboratory. The objective of this dissertation research is to provide an alternative approach that will address the above challenges..
In this research, a label-free assay is designed and studied. Polystyrene (PS), a commonly used polymeric material, serves as the fluorescence agent. Probe ssDNA is covalently immobilized on polystyrene thin film that is supported by a reflecting substrate. When this chip is exposed to excitation light, fluorescence light intensity from PS is detected as the signal. Since the optical constants and conformations of ssDNA and dsDNA (double stranded DNA) are different, the measured fluorescence from PS changes for the same intensity of excitation light.. The fluorescence contrast is used to quantify the amount of probe-target hybridization. A mathematical model that considers multiple reflections and scattering is developed to explain the mechanism of the fluorescence contrast which depends on the thickness of the PS film. Scattering is the dominant factor that contributes to the contrast. The potential of this assay to detect single nucleotide polymorphism is also tested.<br>Ph. D.
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Puckett, James William Grubbs Robert H. Dervan Peter B. "Microarray and genome-wide sequencing approaches to characterizing DNA binding molecules /." Diss., Pasadena, Calif. : California Institute of Technology, 2009. http://resolver.caltech.edu/CaltechETD:etd-05282009-145527.
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Van, Zuydam Natalie Rachel. "Identification of Leptographium species by oligonucleotide discrimination on a DNA microarray." Diss., University of Pretoria, 2009. http://hdl.handle.net/2263/28928.
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Leptographium is an anamorph genus within the Ophiostomatoid group of fungi and represents a unique case for molecular applications. The genus has a near complete sequence data available for three genes across all known species. This characteristic makes it a perfect test group for investigating applications of new diagnostic techniques within ascomycetes. Probes and primers, for microarrays, are designed from phylogenetically useful gene regions and are fabricated onto a solid substrate using printing technology. The sample is prepared using PCR and is hybridised to the probes under stringent conditions. The resulting fluorescent pattern is rigorously analysed to distinguish species from each other. Diagnostic PCR uses primers that are designed in similar way to the way probes are designed for microarrays and indicate the presence of a species through positive amplification. This research methodology will be applied to Leptographium to evaluate the efficacy of microarray technology for discriminating species within that genus. The data gained from this research study will be used in applications for other genera using microarray technology.<br>Dissertation (MSc)--University of Pretoria, 2011.<br>Genetics<br>Unrestricted
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Wu, Chun-Lam Charlene. "Assessment of cell cycle in the condyle using microarray technology /." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31942611.
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Wu, Chun-Lam Charlene, and 胡春琳. "Assessment of cell cycle in the condyle using microarray technology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B45012283.
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Hlatshwayo, Nkosikhona Rejoyce. "Comparison of protein binding microarray derived and ChIP-seq derived transcription factor binding DNA motifs." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1017907.
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Transcription factors (TFs) are biologically important proteins that interact with transcription machinery and bind DNA regulatory sequences to regulate gene expression by modulating the synthesis of the messenger RNA. The regulatory sequences comprise of short conserved regions of a specific length called motifs . TFs have very diverse roles in different cells and play a very significant role in development. TFs have been associated with carcinogenesis in various tissue types, as well as developmental and hormone response disorders. They may be responsible for the regulation of oncogenes and can be oncogenic. Consequently, understanding TF binding and knowing the motifs to which they bind is worthy of attention and research focus. Various projects have made the study of TF binding their main focus; nevertheless, much about TF binding remains confounding. Chromatin immunoprecipitation in conjunction with deep sequencing (ChIP-seq) techniques are a popular method used to investigate DNA-TF interactions in vivo. This procedure is followed by motif discovery and motif enrichment analysis using relevant tools. Protein Binding Microarrays (PBMs) are an in vitro method for investigating DNA-TF interactions. We use a motif enrichment analysis tools (CentriMo and AME) and an empirical quality assessment tool (Area under the ROC curve) to investigate which method yields motifs that are a true representation of in vivo binding. Motif enrichment analysis: On average, ChIP-seq derived motifs from the JASPAR Core database outperformed PBM derived ones from the UniPROBE mouse database. However, the performance of motifs derived using these two methods is not much different from each other when using CentriMo and AME. The E-values from Motif enrichment analysis were not too different from each other or 0. CentriMo showed that in 35 cases JASPAR Core ChIP-seq derived motifs outperformed UniPROBE mouse PBM derived motifs, while it was only in 11 cases that PBM derived motifs outperformed ChIP-seq derived motifs. AME showed that in 18 cases JASPAR Core ChIP-seq derived motifs did better, while only it was only in 3 cases that UniPROBE motifs outperformed ChIP-seq derived motifs. We could not distinguish the performance in 25 cases. Empirical quality assessment: Area under the ROC curve values computations followed by a two-sided t-test showed that there is no significant difference in the average performances of the motifs from the two databases (with 95% confidence, mean of differences=0.0088125 p-value= 0.4874, DF=47) .
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Maughan, Michele Nancy. "Molecular detection and identification of avian influenza viruses by cDNA microarray." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 141 p, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:1440635.
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Leung, Yuk-yee, and 梁玉儀. "An integrated framework for feature selection and classification in microarray data analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43278632.
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Kossenkov, Andrei T̈ozeren Aydin. "Identification of activation of transcription factors from microarray data /." Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/1308.
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Xie, Dan, and 謝丹. "Application of high-throughput tissue microarray technology in cancer research." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30283619.
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Brian, Björn. "Microarray Technology for Kinetic Analysis of Vesicle Bound Receptor-Ligand Interactions." Thesis, Linköping University, The Department of Physics, Chemistry and Biology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8739.
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<p>A proof-of-concept for a novel microarray used to study protein-ligand interaction in real-time using label-free detection is presented. Many of todays commercially available instruments lack the ability to immobilize membrane proteins. At the same time, the pharmaceutical industry develops drugs directed towards membrane-bound receptors. The need to study drug-target kinetics and to be able to screen for new medical substances is high. To study the biomolecular interactions in real-time, imaging surface plasmon resonance (iSPR) is used. A patterned sensor surface with hydrophobic barriers assisting in the piezodispensing of NeutrAvidin with complex-bound biotin-ssDNA is created. Histidine-tagged proteins are immobilized at the vesicle surface using divalent nitrilotriacetic acid. The concept of the vesicle immobilization, the protein-binding to vesicles and the protein-ligand interaction is initially studied using a Biacore instrument. The dissociation of the ligand IFNα2 from its receptor ifnar-2 (wt) are in accordance with the literature. In the imaging SPR experiments, it is found that the dissociation of IFNα2 from the ifnar-2 (wt) receptor is slower than expected, probably due to rebinding of the ligand. It is also found that imidazole is needed to avoid vesicle-vesicle interaction. The immobilization of proteins had to be done on-line i.e. when the vesicles were bound to the surface. Depending on the mixture of receptors at the vesicle surface the affinity for the ligand was changed. The results achieved were reproducible.</p>
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Tai, Ching-wan. "A study on some missing value estimation algorithms for DNA microarray data." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B39364513.
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Partovi, Goli. "Hur kan DNA-microarray användas som metod inom den dermatologiska antiaging forskningen?" Thesis, Umeå universitet, Kemiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-74442.
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