Relevant bibliographies by topics / Microarray and DNA

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Microarray and DNA'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Microarray and DNA"

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Whipple, Mark Eliot, and Winston Patrick Kuo. "DNA Microarrays in Otolaryngology-Head and Neck Surgery." Otolaryngology–Head and Neck Surgery 127, no. 3 (2002): 196–204. http://dx.doi.org/10.1067/mhn.2002.127383.

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OBJECTIVES: Our goal was to review the technologies underlying DNA microarrays and to explore their use in otolaryngology-head and neck surgery. STUDY DESIGN: The current literature relating to microarray technology and methodology is reviewed, specifically the use of DNA microarrays to characterize gene expression. Bioinformatics involves computational and statistical methods to extract, organize, and analyze the huge amounts of data produced by microarray experiments. The means by which these techniques are being applied to otolaryngology-head and neck surgery are outlined. RESULTS: Microarray technologies are having a substantial impact on biomedical research, including many areas relevant to otolaryngology-head and neck surgery. CONCLUSIONS: DNA microarrays allow for the simultaneous investigationof thousands of individual genes in a single experiment. In the coming years, the application of these technologies to clinical medicine should allow for unprecedented methods ofdiagnosis and treatment. SIGNIFICANCE: These highly parallel experimental techniques promise to revolutionize gene discovery, disease characterization, and drug development.
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Jack, Philippa, and David Boyle. "DNA microarrays for pathogen detection and characterisation." Microbiology Australia 27, no. 2 (2006): 68. http://dx.doi.org/10.1071/ma06068.

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DNA microarrays have three main potential diagnostic uses in clinical microbiology: detection of known pathogens, pathogen typing and novel pathogen discovery. Although DNA microarray platforms offer the ability to screen for a large number of agents in parallel, sensitivity is dependent on the ability to obtain adequate amounts of pathogen nucleic acids from collected samples. In general, high levels of sensitivity require a PCR amplification step using specific primer sets, subsequently reducing the overall scope of the microarray assay. At present, relatively high costs, restricted sample throughput capabilities and validation difficulties are also major factors limiting the implementation of DNA microarray assays in diagnostic microbiology laboratories.
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Call, Douglas R., Marlene K. Bakko, Melissa J. Krug, and Marilyn C. Roberts. "Identifying Antimicrobial Resistance Genes with DNA Microarrays." Antimicrobial Agents and Chemotherapy 47, no. 10 (2003): 3290–95. http://dx.doi.org/10.1128/aac.47.10.3290-3295.2003.

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ABSTRACT We developed and tested a glass-based microarray suitable for detecting multiple tetracycline (tet) resistance genes. Microarray probes for 17 tet genes, the β-lactamase bla TEM-1 gene, and a 16S ribosomal DNA gene (Escherichia coli) were generated from known controls by PCR. The resulting products (ca. 550 bp) were applied as spots onto epoxy-silane-derivatized, Teflon-masked slides by using a robotic spotter. DNA was extracted from test strains, biotinylated, hybridized overnight to individual microarrays at 65°C, and detected with Tyramide Signal Amplification, Alexa Fluor 546, and a microarray scanner. Using a detection threshold of 3× the standard deviation, we correctly identified tet genes carried by 39 test strains. Nine additional strains were not known to harbor any genes represented on the microarray, and these strains were negative for all 17 tet probes as expected. We verified that R741a, which was originally thought to carry a novel tet gene, tet(I), actually harbored a tet(G) gene. Microarray technology has the potential for screening a large number of different antibiotic resistance genes by the relatively low-cost methods outlined in this paper.
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Guo, Qingbin M. "DNA microarray and cancer." Current Opinion in Oncology 15, no. 1 (2003): 36–43. http://dx.doi.org/10.1097/00001622-200301000-00005.

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Davies, S. W., and D. A. Seale. "DNA Microarray Stochastic Model." IEEE Transactions on Nanobioscience 4, no. 3 (2005): 248–54. http://dx.doi.org/10.1109/tnb.2005.853665.

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Jia, Kun, Miao Yu, Gui-Hong Zhang, et al. "Detection and identification of Mycobacterium tuberculosis and Mycobacterium bovis from clinical species using DNA microarrays." Journal of Veterinary Diagnostic Investigation 24, no. 1 (2011): 156–60. http://dx.doi.org/10.1177/1040638711417141.

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The objectives of the current study were to evaluate the use of DNA microarray for the rapid and direct detection of Mycobacterium tuberculosis and Mycobacterium bovis in bovine milk, blood, and pharyngeal swab samples, and to compare the use of DNA microarrays with current molecular detection techniques. The present study describes a microarray assay based on mtp40 and pncA gene sequences, which can be used to detect M. tuberculosis and M. bovis species. Each probe was spotted onto a silylated glass slide with an arrayer and used for hybridization with fluorescently labeled DNA derived from amplified DNA samples. The detection limit for mycobacterial DNA using this DNA microarray method was 50 fg (5 tubercle bacilli). Mycobacterium tuberculosis and/or M. bovis was detected in 7.1% (24/336) of the cattle specimens using the DNA microarray compared to 6.0% (20/336) using culture methods. Mixed infections were detected in 3 animals using the DNA microarray method, whereas the mixed infections were detected in 2 animals using either culture or polymerase chain reaction methods. The use of ancillary in vitro tests alongside the DNA microarray enhanced the detection of cattle infected with M. tuberculosis and/or M. bovis and reduced the number of false-positive animals that would be culled. More species may be easily added to this system, and supplementary probes can be added to increase the simultaneous detection power.
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Kostrzynska, M., and A. Bachand. "Application of DNA microarray technology for detection, identification, and characterization of food-borne pathogens." Canadian Journal of Microbiology 52, no. 1 (2006): 1–8. http://dx.doi.org/10.1139/w05-105.

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DNA microarrays represent the latest advance in molecular technology. In combination with bioinformatics, they provide unparalleled opportunities for simultaneous detection of thousands of genes or target DNA sequences and offer tremendous potential for studying food-borne microorganisms. This review provides an up-to-date look at the application of DNA microarray technology to detect food-borne pathogenic bacteria, viruses, and parasites. In addition, it covers the advantages of using microarray technology to further characterize microorganisms by providing information for specific identification of isolates, to understand the pathogenesis based on the presence of virulence genes, and to indicate how new pathogenic strains evolved epidemiologically and phylogenetically.Key words: DNA microarrays, food-borne pathogens, detection.
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Xu, Lizhe, Grace A. Maresh, Jason Giardina, and Seth H. Pincus. "Comparison of Different Microarray Data Analysis Programs and Description of a Database for Microarray Data Management." DNA and Cell Biology 23, no. 10 (2004): 643–51. http://dx.doi.org/10.1089/dna.2004.23.643.

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Valente, Eduardo, and Miguel Rocha. "Integrating data from heterogeneous DNA microarray platforms." Journal of Integrative Bioinformatics 12, no. 4 (2015): 39–55. http://dx.doi.org/10.1515/jib-2015-281.

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Summary DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.
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Chiodi, Elisa, Allison M. Marn, Matthew T. Geib, and M. Selim Ünlü. "The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview." Polymers 13, no. 7 (2021): 1026. http://dx.doi.org/10.3390/polym13071026.

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The importance of microarrays in diagnostics and medicine has drastically increased in the last few years. Nevertheless, the efficiency of a microarray-based assay intrinsically depends on the density and functionality of the biorecognition elements immobilized onto each sensor spot. Recently, researchers have put effort into developing new functionalization strategies and technologies which provide efficient immobilization and stability of any sort of molecule. Here, we present an overview of the most widely used methods of surface functionalization of microarray substrates, as well as the most recent advances in the field, and compare their performance in terms of optimal immobilization of the bioreceptor molecules. We focus on label-free microarrays and, in particular, we aim to describe the impact of surface chemistry on two types of microarray-based sensors: microarrays for single particle imaging and for label-free measurements of binding kinetics. Both protein and DNA microarrays are taken into consideration, and the effect of different polymeric coatings on the molecules’ functionalities is critically analyzed.
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Dissertations / Theses on the topic "Microarray and DNA"

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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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Books on the topic "Microarray and DNA"

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Tuimala, Jarno, and M. Minna Laine. DNA microarray data analysis. CSC - Scientific Computing, 2003.

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Microarray analysis. Wiley-Liss, 2003.

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Next generation microarray bioinformatics: Methods and protocols. Humana Press, 2012.

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Dubitzky, Werner, Daniel P. Berrar, and Martin Granzow. A practical approach to microarray data analysis. Springer, 2009.

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Knudsen, Steen. Guide to Analysis of DNA Microarray Data. John Wiley & Sons, Ltd., 2005.

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Steen, Knudsen, ed. Guide to analysis of DNA microarray data. 2nd ed. Wiley-Liss, 2004.

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Lee, Mei-Ling Ting. Analysis of microarray gene expression data. Kluwer Academic, 2004.

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Kellogg, Valerie. The surging microarray biochip business. Business Communications Co., 2001.

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Knudsen, Steen. Guide to Analysis of DNA Microarray Data. John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471670278.

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Knudsen, Steen. A biologist's guide to analysis of DNA microarray data. Wiley-Liss, 2003.

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Book chapters on the topic "Microarray and DNA"

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Charpe, Ashwini M. "DNA Microarray." In Advances in Biotechnology. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1554-7_6.

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Braileanu, Gheorghe T. "DNA Microarray Analysis." In Reproductive Endocrinology. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-88186-7_10.

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Zhou, Jizhong, and Dorothea K. Thompson. "DNA Microarray Technology." In Microbial Functional Genomics. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471647527.ch6.

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Marzancola, Mahsa Gharibi, Abootaleb Sedighi, and Paul C. H. Li. "DNA Microarray-Based Diagnostics." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3136-1_12.

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Dufva, Martin. "Fabrication of DNA Microarray." In Methods in Molecular Biology. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-538-1_5.

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Garzon, Max H., Vinhthuy Phan, Kiran C. Bobba, and Raghuver Kontham. "Sensitivity and Capacity of Microarray Encodings." In DNA Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11753681_7.

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Fischer, Nicholas O., and Theodore M. Tarasow. "Identification and Optimization of DNA Aptamer Binding Regions Using DNA Microarrays." In Protein Microarray for Disease Analysis. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-043-0_5.

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Rantala-Ylinen, Anne, Kaarina Sivonen, Annick Wilmotte, Hans C. P. Matthijs, and J. Merijn Schuurmans. "DNA (Diagnostic) and cDNA Microarray." In Molecular Tools for the Detection and Quantification of Toxigenic Cyanobacteria. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119332169.ch8.

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Karnani, Neerja, Christopher M. Taylor, and Anindya Dutta. "Microarray Analysis of DNA Replication Timing." In Microarray Analysis of the Physical Genome. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-192-9_14.

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Hung, She-Pin, Suman Sundaresh, Pierre F. Baldi, and G. Wesley Hatfield. "Understanding DNA Microarrays: Sources and Magnitudes of Variances in DNA Microarray Data Sets." In Genomics, Proteomics and Vaccines. John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470012536.ch4.

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Conference papers on the topic "Microarray and DNA"

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Martins, Diogo, Xi Wei, Rastislav Levicky, and Yong-Ak Song. "Accelerating the Mass Transport of DNA Biomolecules Onto DNA Microarray for Enhanced Detection by Electrokinetic Concentration in a Microfluidic Chip." In ASME 2016 5th International Conference on Micro/Nanoscale Heat and Mass Transfer. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/mnhmt2016-6562.

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Abstract:
Morpholinos (MOs) are synthetic nucleic acids analogues with a non-charged backbone of morpholine rings. To enhance the MO-DNA hybridization assay speed, we propose the integration of a MO microarray with an ion concentration polarization (ICP) based microfluidic concentrator. The ICP concentrator collects target biomolecules from a ∼μL fluidic DNA sample and concentrates them electrokinetically into a ∼nL plug located in the vicinity of the MO probes. ICP preconcentration not only reduces the analyte diffusion length but also increases the binding reaction rate, and as a result, ICP-enhanced MO microarrays allow much faster hybridization than standard diffusion-limited MO microarrays.
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Hern´ndez-Cabronero, Miguel, Juan Munoz-Gomez, Ian Blanes, Michael W. Marcellin, and Joan Serra-Sagrista. "DNA Microarray Image Coding." In 2012 Data Compression Conference (DCC). IEEE, 2012. http://dx.doi.org/10.1109/dcc.2012.11.

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Liu, Yihui. "Feature Extraction for DNA Microarray Data." In Twentieth IEEE International Symposium on Computer-Based Medical Systems. IEEE, 2007. http://dx.doi.org/10.1109/cbms.2007.49.

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"MICROARRAY SYSTEM - A System for Managing Data Produced by DNA-microarray Experiments." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003137202930296.

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Augustyniak, M., C. Paulus, R. Brederlow, et al. "A 24x16 CMOS-Based Chronocoulometric DNA Microarray." In 2006 IEEE International Solid-State Circuits Conference. Digest of Technical Papers. IEEE, 2006. http://dx.doi.org/10.1109/isscc.2006.1696034.

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Patra, Jagdish C., Nyttle V. George, and Pramod K. Meher. "DNA microarray analysis using Equalized Orthogonal Mapping." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596705.

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Pizzolante, Raffaele, Arcangelo Castiglione, Bruno Carpentieri, Alfredo De Santis, Francesco Palmieri, and Aniello Castiglione. "Format-Independent Protection of DNA Microarray Images." In 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2015. http://dx.doi.org/10.1109/3pgcic.2015.138.

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Pizzolante, Raffaele, Arcangelo Castiglione, Bruno Carpentieri, Alfredo De Santis, and Aniello Castiglione. "Reversible Copyright Protection for DNA Microarray Images." In 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2015. http://dx.doi.org/10.1109/3pgcic.2015.139.

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Lixin Han and Hong Yan. "Fuzzy biclustering for DNA microarray data analysis." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630513.

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Ahmad, Maziidah Mukhtar, Asral Bahari Jambek, and Mohd Yusoff bin Mashor. "Image gridding algorithm for DNA microarray analyser." In 2016 3rd International Conference on Electronic Design (ICED). IEEE, 2016. http://dx.doi.org/10.1109/iced.2016.7804687.

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Reports on the topic "Microarray and DNA"

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WERNER-WASHBURNE, MARGARET, and GEORGE S. DAVIDSON. DNA Microarray Technology. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/791894.

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Wu, Chi-Fang, James J. Valdes, Jennifer W. Sekowski, and William E. Bentley. Identification of Multiple Pathogenic Bacteria Using a DNA Microarray. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408810.

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O'Malley, Karen L. Fundamental Patterns Underlying Neurotoxicity Revealed by DNA Microarray Expression Profiling. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada409422.

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O'Malley, Karen L. Fundamental Patterns Underlying Neurotoxicity Revealed by DNA Microarray Expression Profiling. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada429295.

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Khatri, Purvesh, Dechang Chen, Jaques Reifman, Craig M. Lilly, and Larry A. Sonna. Software Tool for Analysis of Variance of DNA Microarray Data. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada460048.

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Wu, Liyou, T. Y. Yi, Joy Van Nostrand, and Jizhong Zhou. Phylogenetic Analysis of Shewanella Strains by DNA Relatedness Derived from Whole Genome Microarray DNA-DNA Hybridization and Comparison with Other Methods. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/986917.

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Liao, James C., and Vwani Roychawdhury. DNA MIcroarray-Assisted Modeling of Metabolic and Regulatory Networks With Applications to Bio-Defense. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada457394.

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Beer, N., B. Baker, T. Piggott, et al. Hybridization and Selective Release of DNA Microarrays. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1033734.

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Martin, Jennifer A., Yaroslav Chushak, Jorge C. Benavides, Joshua Hagen, and Nancy Kelley-Loughnane. DNA Microarrays for Aptamer Identification and Structural Characterization. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada597207.

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Gregory Stephanopoulos. Development of DNA Microarrays for Metabolic Pathway and Bioprocess Monitoring. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/837870.

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