Relevant bibliographies by topics / Microarray

Contents

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

Academic literature on the topic 'Microarray'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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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 (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 (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 mo
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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 (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 limitation
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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 (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. T
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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 (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 predic
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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: Microarr
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Maziidah Mukhtar Ahmad and Asral Bahari Jambek. "Efficient Hexagonal Gridding Method for DNA Microarray Image." International Journal of Advanced Communication Technology (IJACT) 4 (November 19, 2024): 1–18. https://doi.org/10.58915/ijact.v4.2024.767.

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In genetics, a deoxyribonucleic acid (DNA) microarray is a useful instrument that is frequently used to track thousands of genes' expression levels simultaneously. For DNA microarray, gene expression is done through microarray spot gridding, segmentation and intensity extraction. The gridding processes identify each microarray spot location and supply their coordinates for the spot. Many microarray technologies arrange their microarray spots in in rectangular fashion. However, a hexagonal grid arrangement is also being used to increase the density of the spot per unit area. While gridding micr
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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
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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 (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 o
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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 (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)-b
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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) A
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Wang, Tao. "Statistical design and analysis of microarray experiments." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains ix, 146 p.; also includes graphics (some col.) Includes bibliographical references (p. 145-146). Available online via OhioLINK's ETD Center
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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 pa
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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 adj
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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 experi
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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.<br>Includes bibliographical references (leaf 32, first group).<br>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 app
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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. 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. 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. Humana Press, 2007. http://dx.doi.org/10.1385/1597453900.

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Agapito, Giuseppe, ed. Microarray Data Analysis. 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. 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. 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. 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. John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471728543.

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Guzzi, Pietro Hiram, ed. Microarray Data Analysis. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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Niu, Chao, Hao Zhang, and Linhang Li. "Microarray Fluorescence Image Processing and Analysis." In 2024 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO). IEEE, 2024. https://doi.org/10.1109/3m-nano61605.2024.10769626.

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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
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Gruhler, Holger, Nicolaus Hey, Martin Müller, et al. "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
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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. 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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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), 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), 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), 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 understand
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Jornsten, Rebeka, Bin Yu, Wei Wang, and Kannan Ramchandran. Compression of cDNA and Inkjet Microarray Images. Defense Technical Information Center, 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. Iowa State University, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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), 2003. http://dx.doi.org/10.2172/823317.

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Couture, Oliver, Keith Callenberg, Neeraj Koul, et al. ANEXdb: An Integrated Animal Annotation and Microarray EXpression Database. Iowa State University, 2010. http://dx.doi.org/10.31274/ans_air-180814-946.

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