Relevant bibliographies by topics / Microarray

Academic literature on the topic 'Microarray'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Microarray"

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Handley, Daniel, Nicoleta Serban, David G. Peters, and Clark Glymour. "Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 6, 2004): 1–2. http://dx.doi.org/10.2202/1544-6115.1091.

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We discuss our concerns regarding the reliability of data generated by spotted cDNA microarrays. Two types of error we highlight are cross-hybridization artifact due to sequence homologies and sequence errors in the cDNA used for spotting on microarrays. We feel that statisticians who analyze microarray data should be aware of these sources of unreliability intrinsic to cDNA microarray design and use.
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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 (March 26, 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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BS, Shreenidhi, and Saravanakumar Ramachandran. "Microarray image enhancement techniques by denoising: Current status and future directions." International Journal of Natural Sciences Research 11, no. 1 (June 12, 2023): 44–51. http://dx.doi.org/10.18488/63.v11i1.3393.

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Microarray imaging is a technique for simultaneously detecting the expression of numerous genes. Microarrays are simply a slide with unique Deoxyribonucleic Acid (DNA) probes, often known as gene chips. This paper aims to provide an overview of important contributions to the field of image denoising in the context of microarray imaging. The methodologies discussed in the article include various techniques of transform domain and spatial filtering methods for denoising microarray images that can be used to improve the quality of microarray images. We have identified the strengths and limitations of these techniques and highlights their potential impact. The paper also explores future developments in this area and discusses their potential impact, on microarray imaging.
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Raczynski, Lech, Krzysztof Wozniak, Tymon Rubel, and Krzysztof Zaremba. "Application of Density Based Clustering to Microarray Data Analysis." International Journal of Electronics and Telecommunications 56, no. 3 (September 1, 2010): 281–86. http://dx.doi.org/10.2478/v10177-010-0037-9.

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Application of Density Based Clustering to Microarray Data AnalysisIn just a few years, gene expression microarrays have rapidly become a standard experimental tool in the biological and medical research. Microarray experiments are being increasingly carried out to address the wide range of problems, including the cluster analysis. The estimation of the number of clusters in datasets is one of the main problems of clustering microarrays. As a supplement to the existing methods we suggest the use of a density based clustering technique DBSCAN that automatically defines the number of clusters. The DBSCAN and other existing methods were compared using the microarray data from two datasets used for diagnosis of leukemia and lung cancer.
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Liang, Mingyu, Amy G. Briggs, Elizabeth Rute, Andrew S. Greene, and Allen W. Cowley. "Quantitative assessment of the importance of dye switching and biological replication in cDNA microarray studies." Physiological Genomics 14, no. 3 (August 15, 2003): 199–207. http://dx.doi.org/10.1152/physiolgenomics.00143.2002.

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Dye switching and biological replication substantially increase the cost and the complexity of cDNA microarray studies. The objective of the present analysis was to quantitatively assess the importance of these procedures to provide a quantitative basis for decision-making in the design of microarray experiments. Taking advantage of the unique characteristics of a published data set, the impact of these procedures on the reliability of microarray results was calculated. Adding a second microarray with dye switching substantially increased the correlation coefficient between observed and predicted ln(ratio) values from 0.38 ± 0.06 to 0.62 ± 0.04 ( n = 12) and the outlier concordance from 21 ± 3% to 43 ± 4%. It also increased the correlation with the entire set of microarrays from 0.60 ± 0.04 to 0.79 ± 0.04 and the outlier concordance from 31 ± 6% to 58 ± 5% and tended to improve the correlation with Northern blot results. Adding a second microarray to include biological replication also improved the performance of these indices but often to a lesser degree. Inclusion of both procedures in the second microarray substantially improved the consistency with the entire set of microarrays but had minimal effect on the consistency with predicted results. Analysis of another data set generated using a different cDNA labeling method also supported a significant impact of dye switching. In conclusion, both dye switching and biological replication substantially increased the reliability of microarray results, with dye switching likely having even greater benefits. Recommendations regarding the use of these procedures were proposed.
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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 (September 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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García-Albert, L., F. Martín-Sánchez, A. García-Sáiz, and G. H. López-Campos. "Analysis and Management of HIV Peptide Microarray Experiments." Methods of Information in Medicine 45, no. 02 (2006): 158–62. http://dx.doi.org/10.1055/s-0038-1634060.

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Summary Objectives: To develop a tool for then easy and user-friendly management of peptide microarray experiments and for the use of the results of these experiments for the study the immune response against HIV virus infection in clinical samples. Methods: Applying bioinformatics and statistics for the analysis of data coming from microarray experiments as well as implementing a MIAME (Minimum Information About a Microarray Experiment) compliant database for managing and annotating experiments, results and samples. Results: We present a new tool for managing not only nucleic acid microarray experiments but also protein microarray experiments. From the analysis of experimental data, we can detect different profiles in the reactivity of the sera with different genotypes. Conclusions: We have developed a new tool for managing microarray data including clinical annotations for the samples as well as the capability of annotating other microarray formats different to those based on nucleic acids. The use of peptide microarrays and bioinformatics analysis opens a new scope for the characterization of the immune response, and analyzing and identifying the humoral response of viruses with different genotypes.
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White, Christine A., and Lois A. Salamonsen. "A guide to issues in microarray analysis: application to endometrial biology." Reproduction 130, no. 1 (July 2005): 1–13. http://dx.doi.org/10.1530/rep.1.00685.

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Within the last decade, the development of DNA microarray technology has enabled the simultaneous measurement of thousands of gene transcripts in a biological sample. Conducting a microarray study is a multi-step process; starting with a well-defined biological question, moving through experimental design, target RNA preparation, microarray hybridisation, image acquisition and data analysis – finishing with a biological interpretation requiring further study. Advances continue to be made in microarray quality and methods of statistical analysis, improving the reliability and therefore appeal of microarray analysis for a wide range of biological questions. The purpose of this review is to provide both an introduction to microarray methodology, as well as a practical guide to the use of microarrays for gene expression analysis, using endometrial biology as an example of the applications of this technology. While recommendations are based on previous experience in our laboratory, this review also summarises the methods currently considered to be best practice in the field.
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Liu, Yan. "Neoglycolipid (NGL)-based oligosaccharide microarrays and highlights of their recent applications in studies of the molecular basis of pathogen–host interactions." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1361–67. http://dx.doi.org/10.1042/bst0381361.

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Carbohydrate microarray technologies are new developments at the frontier of glycomics that are showing great promise as tools for high-throughput analysis of carbohydrate-mediated interactions and the elucidation of carbohydrate ligands involved not only in endogenous receptor systems, but also pathogen–host interactions. The main advantage of microarray analysis is that a broad range of glycan sequences can be immobilized on solid matrices as minute spots and simultaneously interrogated. Different methodologies have emerged for constructing carbohydrate microarrays. The NGL (neoglycolipid)-based oligosaccharide microarray platform is among the relatively few systems that are beyond proof-of-concept and have provided new biological information. In the present article, I dwell, in some detail, on the NGL-based microarray. Highlights are the recent applications of NGL-based microarrays that have contributed to knowledge on the molecular basis of pathogen–host interactions, namely the assignments of the carbohydrate-binding specificities of several key surface-adhesive proteins of Toxoplasma gondii and other apicomplexan parasites, and the elucidation of receptor-binding specificities of the pandemic influenza A (H1N1) 2009 (H1N1pdm) virus compared with seasonal H1N1 virus.
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Paredes, Carlos J., Ryan S. Senger, Iwona S. Spath, Jacob R. Borden, Ryan Sillers, and Eleftherios T. Papoutsakis. "A General Framework for Designing and Validating Oligomer-Based DNA Microarrays and Its Application to Clostridium acetobutylicum." Applied and Environmental Microbiology 73, no. 14 (May 25, 2007): 4631–38. http://dx.doi.org/10.1128/aem.00144-07.

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ABSTRACT While DNA microarray analysis is widely accepted as an essential tool for modern biology, its use still eludes many researchers for several reasons, especially when microarrays are not commercially available. In that case, the design, construction, and use of microarrays for a sequenced organism constitute substantial, time-consuming, and expensive tasks. Recently, it has become possible to construct custom microarrays using industrial manufacturing processes, which offer several advantages, including speed of manufacturing, quality control, no up-front setup costs, and need-based microarray ordering. Here, we describe a strategy for designing and validating DNA microarrays manufactured using a commercial process. The 22K microarrays for the solvent producer Clostridium acetobutylicum ATCC 824 are based on in situ-synthesized 60-mers employing the Agilent technology. The strategy involves designing a large library of possible oligomer probes for each target (i.e., gene or DNA sequence) and experimentally testing and selecting the best probes for each target. The degenerate C. acetobutylicum strain M5 lacking the pSOL1 megaplasmid (with 178 annotated open reading frames [genes]) was used to estimate the level of probe cross-hybridization in the new microarrays and to establish the minimum intensity for a gene to be considered expressed. Results obtained using this microarray design were consistent with previously reported results from spotted cDNA-based microarrays. The proposed strategy is applicable to any sequenced organism.
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Dissertations / Theses on the topic "Microarray"

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Pernagallo, Salvatore. "Biocompatible polymer microarrays for cellular high-content screening." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/7571.

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The global aim of this thesis was to study the use of microarray technology for the screening and identification of biocompatible polymers, to understand physiological phenomena, and the design of biomaterials, implant surfaces and tissue-engineering scaffolds. This work was based upon the polymer microarray platform developed by the Bradley group. Polymer microarrays were successfully applied to find the best polymer supports for: (i) mouse fibroblast cells and used to evaluate cell biocompatibility and cell morphology. Fourteen polyurethanes demonstrated significant cellular adhesion. (ii) Analysis of the adhesion of human erythroleukaemic K562 suspension cells onto biomaterials with particular families of polyurethanes and polyacrylates identified. A DNA microarray study (to access the global gene expression profiles upon cellular binding) demonstrated that interactions between cells and some polyacrylates induced a number of transcriptomic changes. These results suggested that, during these interactions, a chain of cellular changes is triggered, most notably resulting in the downregulation of membrane receptors and ligands. (iii) Identification of polymers with potential applications in the field of stem cell biology. Polymers were identified that showed attachment, promotion and stabilisation of hepatocyte-like cells. A polyurethane support (PU-134) was pinpointed, which significantly improved both hepatocyte-like cell function and “lifespan”. A second project investigated biomaterials that promoted adhesion, growth and function of endothelial progenitor cells. A new polymer matrix was identified which contained the necessary signals to promote endothelial phenotype and function. This has potential application in the creation of blood vessels and the endothelialisation of artificial vessel prostheses and stent coatings for improving angioplasty therapy. (iv) The study of bacterial adhesion, focusing on the adhesion of food-borne pathogenic bacterium Salmonella enterica serovar typhimurium, strain SL1344, and the commensal bacterium Escherichia coli, strain W3110. Several polymers were found to support selective bacterial enrichment, as well as others that minimised bacterial adhesion.
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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.
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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Klenkar, Goran. "Protein Microarray Chips." Doctoral thesis, Linköping : Univ, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8904.

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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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Dvergsten, Erik C. "A Weighted Gene Co-expression Network Analysis for Streptococcus sanguinis Microarray Experiments." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4430.

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Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes. Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene modules were determined from the connection strengths, and various network connectivity measures were calculated. S. sanguinis gene expression profile data was loaded for 2272 genes and 14 samples with 3 replicates each. The soft thresholding power β=6 was chosen to maximize R2 while maintaining a high mean number of connections. Nine modules were found. Possible meta-modules were found to be: Module 1: Blue & Green, Module 2: Pink, Module 3: Yellow, Brown & Red, Module 4: Black, Module 5: Magenta & Turquoise. The absolute value of module membership was found to be highly positively correlated with intramodular connectivity. Each of the nine modules were examined. Two methods (intramodular connectivity and TOM-based connectivity followed by network mapping) for identifying candidate hub genes were performed. Most modules provided similar results between the two methods. Similar rankings between the two methods can be considered equivalent and both can be used to detect candidate hub genes. Gene ontology information was unavailable to help select a module of interest. This network analysis would help researchers create new research hypotheses and design experiments for validation of candidate hub genes in biologically important modules.
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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.
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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Downes, Aidan Rawle. "Microarray submissions to Experibase." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33281.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaf 32, first group).
Experibase is an experimental database that supports the storage of data from leading biological experiment techniques. Experibase ontology was extended to include a robust representation of microarray data, a leading experimental technique. The microarray submission system takes advantage of Experibase's new microarray storage capabilities by allowing biologist to submit microarray data to Experibase using an application that they are already familiar with. The transformation of data from the submitted format to a format suitable for Experibase takes place without the submitter's knowledge, reducing the need for an Experibase specific submission application.
by Aidan Rawle Downes.
M.Eng.
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Mao, Shihong. "Comparative Microarray Data Mining." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.

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Stephens, Nathan Wallace. "A Comparison of Microarray Analyses: A Mixed Models Approach Versus the Significance Analysis of Microarrays." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1115.

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DNA microarrays are a relatively new technology for assessing the expression levels of thousands of genes simultaneously. Researchers hope to find genes that are differentially expressed by hybridizing cDNA from known treatment sources with various genes spotted on the microarrays. The large number of tests involved in analyzing microarrays has raised new questions in multiple testing. Several approaches for identifying differentially expressed genes have been proposed. This paper considers two: (1) a mixed models approach, and (2) the Signiffcance Analysis of Microarrays.
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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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Books on the topic "Microarray"

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Bolón-Canedo, Verónica, and Amparo Alonso-Betanzos, eds. Microarray Bioinformatics. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9442-7.

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Li, Paul C. H., Abootaleb Sedighi, and Lin Wang, eds. Microarray Technology. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3136-1.

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Michael, Korenberg J. Microarray Data Analysis. New Jersey: Humana Press, 2007. http://dx.doi.org/10.1385/1597453900.

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Agapito, Giuseppe, ed. Microarray Data Analysis. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1839-4.

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Lee, Moo-Yeal, ed. Microarray Bioprinting Technology. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46805-1.

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Korenberg, Michael J., ed. Microarray Data Analysis. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5.

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Zhang, Wei, Ilya Shmulevich, and Jaakko Astola. Microarray Quality Control. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471728543.

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Zhang, Wei, Ilya Shmulevich, and Jaakko Astola. Microarray Quality Control. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471728543.

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Guzzi, Pietro Hiram, ed. Microarray Data Analysis. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3173-6.

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Dev, Kambhampati, ed. Protein microarray technology. Weinheim: Wiley-VCH, 2004.

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Book chapters on the topic "Microarray"

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Ma, Kuo-Sheng, Yanchen Wang, Lucas Prater, and Chunlei Wang. "Microarray." In Encyclopedia of Nanotechnology, 1–9. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-007-6178-0_101023-1.

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Debnath, Mousumi, Godavarthi B. K. S. Prasad, and Prakash S. Bisen. "Microarray." In Molecular Diagnostics: Promises and Possibilities, 193–208. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3261-4_13.

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Ma, Kuo-Sheng, Yanchen Wang, Lucas Prater, and Chunlei Wang. "Microarray." In Encyclopedia of Nanotechnology, 2129–37. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-017-9780-1_101023.

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Page, Grier P., and Xiangqin Cui. "Microarray." In Methods and Applications of Statistics in Clinical Trials, 392–415. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118596333.ch23.

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Pal, Debojyoti. "Microarray." In Encyclopedia of Animal Cognition and Behavior, 1–4. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47829-6_170-1.

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Pal, Debojyoti. "Microarray." In Encyclopedia of Animal Cognition and Behavior, 4235–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_170.

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Panicker, Resmi C., Hongyan Sun, Grace Y. J. Chen, and Shao Q. Yao. "Peptide-Based Microarray." In Microarrays, 139–67. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-72719-6_7.

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Lemkin, Peter F., Gregory C. Thornwall, and Jai Evans. "Microarray Analysis Using the MicroArray Explorer." In Statistics for Biology and Health, 229–53. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-21679-0_10.

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Page, Grier P., Stanislav O. Zakharkin, Kyoungmi Kim, Tapan Mehta, Lang Chen, and Kui Zhang. "Microarray Analysis." In Topics in Biostatistics, 409–30. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_20.

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Loewe, Robert P., and Peter J. Nelson. "Microarray Bioinformatics." In Methods in Molecular Biology, 295–320. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-59745-551-0_18.

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Conference papers on the topic "Microarray"

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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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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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Gruhler, Holger, Nicolaus Hey, Martin Müller, Stefan Békési, Michael Freygang, Hermann Sandmaier, and Roland Zengerle. "Topspot: A New Method for the Fabrication of Biochips." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0299.

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Abstract We present a new method for generating microarrays of liquid droplets. This is of basic importance for the fabrication of so called biochips. To generate a microarray we use a print-module containing 24 nozzles. Each nozzle is connected to one of 24 different reservoirs on the same print-module. By applying a high acceleration to the print-module it can be achieved that all of the 24 nozzles eject a small droplet at the same time. This effect is due to the inertia of the liquid inside the print-module. This new method makes the production of low and medium density biochips much faster and significantly cheaper.
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Yang, Youngik, Jong Youl Choi, Kwangmin Choi, Marlon Pierce, Dennis Gannon, and Sun Kim. "BioVLAB-Microarray: Microarray Data Analysis in Virtual Environment." In 2008 IEEE Fourth International Conference on eScience (eScience). IEEE, 2008. http://dx.doi.org/10.1109/escience.2008.57.

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LIATSIS, P., and M. A. NAZARBOLAND. "MICROARRAY IMAGE ANALYSIS." In Proceedings of the 9th International Workshop on Systems, Signals and Image Processing. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776266_0078.

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Wang, Wenkui, Min Liu, and Qiquan Hu. "Novel microarray scanner." In Photonics Asia 2002, edited by Britton Chance, Mingzhe Chen, and Gilwon Yoon. SPIE, 2002. http://dx.doi.org/10.1117/12.482983.

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Park, Hyun Seok. "Mining a logical set of microarray data from heterogeneous multi-platform microarrays." In the 2nd international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1352793.1352911.

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Tozduman, Ersin, and Songul Albayrak. "cDNA microarray image analysis." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130308.

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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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Skovsen, E., M. Duroux, M. T. Neves-Petersen, L. Duroux, and S. B. Petersen. "Photonics and microarray technology." In International Congress on Optics and Optoelectronics, edited by Francesco Baldini, Jiri Homola, Robert A. Lieberman, and Miroslav Miler. SPIE, 2007. http://dx.doi.org/10.1117/12.722927.

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Hatem, Ayat, Kamer Kaya, and Ümit V. Çatalyürek. "Microarray vs. RNA-Seq." In the ACM Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2382936.2382994.

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Reports on the topic "Microarray"

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

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Rohde, Rachel M., and Jerilyn Ann Timlin. Examining microarray slide quality for the EPA using SNL's hyperspectral microarray scanner. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/875988.

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Singh, Anup K., Daniel J. Throckmorton, Jose C. Moran-Mirabal, Joshua B. Edel, Grant D. Meyer, and Harold G. Craighead. Lipid Microarray Biosensor for Biotoxin Detection. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/1141263.

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Splitter, Gary, and Menachem Banai. Microarray Analysis of Brucella melitensis Pathogenesis. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7709884.bard.

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Original Objectives 1. To determine the Brucella genes that lead to chronic macrophage infection. 2. To identify Brucella genes that contribute to infection. 3. To confirm the importance of Brucella genes in macrophages and placental cells by mutational analysis. Background Brucella spp. is a Gram-negative facultative intracellular bacterium that infects ruminants causing abortion or birth of severely debilitated animals. Brucellosis continues in Israel, caused by B. melitensis despite an intensive eradication campaign. Problems with the Rev1 vaccine emphasize the need for a greater understanding of Brucella pathogenesis that could improve vaccine designs. Virulent Brucella has developed a successful strategy for survival in its host and transmission to other hosts. To invade the host, virulent Brucella establishes an intracellular niche within macrophages avoiding macrophage killing, ensuring its long-term survival. Then, to exit the host, Brucella uses placenta where it replicates to high numbers resulting in abortion. Also, Brucella traffics to the mammary gland where it is secreted in milk. Missing from our understanding of brucellosis is the surprisingly lillie basic information detailing the mechanisms that permit bacterial persistence in infected macrophages (chronic infection) and dissemination to other animals from infected placental cells and milk (acute infection). Microarray analysis is a powerful approach to determine global gene expression in bacteria. The close genomic similarities of Brucella species and our recent comparative genomic studies of Brucella species using our B. melitensis microarray, suqqests that the data obtained from studying B. melitensis 16M would enable understanding the pathogenicity of other Brucella organisms, particularly the diverse B. melitensis variants that confound Brucella eradication in Israel. Conclusions Results from our BARD studies have identified previously unknown mechanisms of Brucella melitensis pathogenesis- i.e., response to blue light, quorum sensing, second messenger signaling by cyclic di-GMP, the importance of genomic island 2 for lipopolysaccharide in the outer bacterial membrane, and the role of a TIR domain containing protein that mimics a host intracellular signaling molecule. Each one of these pathogenic mechanisms offers major steps in our understanding of Brucella pathogenesis. Strikingly, our molecular results have correlated well to the pathognomonic profile of the disease. We have shown that infected cattle do not elicit antibodies to the organisms at the onset of infection, in correlation to the stealth pathogenesis shown by a molecular approach. Moreover, our field studies have shown that Brucella exploit this time frame to transmit in nature by synchronizing their life cycle to the gestation cycle of their host succumbing to abortion in the last trimester of pregnancy that spreads massive numbers of organisms in the environment. Knowing the bacterial mechanisms that contribute to the virulence of Brucella in its host has initiated the agricultural opportunities for developing new vaccines and diagnostic assays as well as improving control and eradication campaigns based on herd management and linking diagnosis to the pregnancy status of the animals. Scientific and Agricultural Implications Our BARD funded studies have revealed important Brucella virulence mechanisms of pathogenesis. Our publication in Science has identified a highly novel concept where Brucella utilizes blue light to increase its virulence similar to some plant bacterial pathogens. Further, our studies have revealed bacterial second messengers that regulate virulence, quorum sensing mechanisms permitting bacteria to evaluate their environment, and a genomic island that controls synthesis of its lipopolysaccharide surface. Discussions are ongoing with a vaccine company for application of this genomic island knowledge in a Brucella vaccine by the U.S. lab. Also, our new technology of bioengineering bioluminescent Brucella has resulted in a spin-off application for diagnosis of Brucella infected animals by the Israeli lab by prioritizing bacterial diagnosis over serological diagnosis.
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Jornsten, Rebeka, Bin Yu, Wei Wang, and Kannan Ramchandran. Compression of cDNA and Inkjet Microarray Images. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada407645.

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Rosa, Artur J. M., Gang Ren, and James M. Reecy. Development of the BoviAnalyser cDNA Bovine Microarray. Ames (Iowa): Iowa State University, January 2004. http://dx.doi.org/10.31274/ans_air-180814-592.

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Gottardo, Raphael, Adrian E. Raftery, Ka Yee Yeung, and Roger E. Bumgarner. Robust Estimation of cDNA Microarray Intensities with Replicates. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459797.

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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. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada408810.

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Haghighi, F. Kernel Principle Component Analysis of Microarray Data. Final Report. Office of Scientific and Technical Information (OSTI), November 2003. http://dx.doi.org/10.2172/823317.

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Couture, Oliver, Keith Callenberg, Neeraj Koul, Sushain Pandit, Remy Younes, Zhi-Liang Hu, Jack C. M. Dekkers, James M. Reecy, Vasant G. Honavar, and Christopher K. Tuggle. ANEXdb: An Integrated Animal Annotation and Microarray EXpression Database. Ames (Iowa): Iowa State University, January 2010. http://dx.doi.org/10.31274/ans_air-180814-946.

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