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Letteratura scientifica selezionata sul tema "Microarrays"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Microarrays"

Aparna, G. M., and Kishore K. R. Tetala. "Recent Progress in Development and Application of DNA, Protein, Peptide, Glycan, Antibody, and Aptamer Microarrays." Biomolecules 13, no. 4 (2023): 602. http://dx.doi.org/10.3390/biom13040602.

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Microarrays are one of the trailblazing technologies of the last two decades and have displayed their importance in all the associated fields of biology. They are widely explored to screen, identify, and gain insights on the characteristics traits of biomolecules (individually or in complex solutions). A wide variety of biomolecule-based microarrays (DNA microarrays, protein microarrays, glycan microarrays, antibody microarrays, peptide microarrays, and aptamer microarrays) are either commercially available or fabricated in-house by researchers to explore diverse substrates, surface coating, i

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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 (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 mic

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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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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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Fesseha, Haben, and Hiwot Tilahun. "Principles and Applications of Deoxyribonucleic Acid Microarray: A Review." Pathology and Laboratory Medicine – Open Journal 3, no. 1 (2021): 1–9. http://dx.doi.org/10.17140/plmoj-3-109.

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Deoxyribonucleic acid (DNA) microarrays are collections of DNA probes arranged on a base pair and the latest commercialized molecular diagnostic technologies that offer high throughput results, more sensitive and require less time. It is the most reliable and widely accepted tool facilitating the simultaneous identification of thousands of genetic elements even a single gene. Microarrays are powerful new tools for the investigation of global changes in gene expression profiles in cells and tissues. The different types of DNA microarray or DNA chip devices and systems are described along with t

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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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Korbelik, J., M. Cardeno, J. P. Matisic, A. C. Carraro, and C. MacAulay. "Cytology Microarrays." Analytical Cellular Pathology 29, no. 5 (2007): 435–42. http://dx.doi.org/10.1155/2007/258297.

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The use of high throughput genetic and expression platforms are generating many candidate diagnostic markers and therapeutic targets for a wide variety of clinical conditions. Tissue microarrays can be used for the evaluation of the utility of many of these markers. However, tissue microarrays can suffer from the limitations associated with sampling and sectioning tissues. We introduce a novel microarray technique based on cell suspensions. Multiple slides can be made, all of which are equally representative of the initial sample. A robotic device was designed that can deposit 60 distinct spot

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Wilson, K. J., and E. de la Vega. "The potential of microarrays to assist shrimp breeding and production: a review." Australian Journal of Experimental Agriculture 45, no. 8 (2005): 901. http://dx.doi.org/10.1071/ea05060.

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The shrimp aquaculture industry is a relatively new livestock industry, having developed over the past 30 years. Thus, it is poised to take advantage of new technologies from the outset of selective breeding programs. This contrasts with long established livestock industries, where there are already highly specialised breeds. This review focuses specifically on the potential application of microarrays to shrimp breeding. Potential applications of microarrays in selective breeding programs are summarised. Microarrays can be used as a rapid means to generate molecular markers for genetic linkage

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Trost, Brett, Catherine A. Moir, Zoe E. Gillespie, Anthony Kusalik, Jennifer A. Mitchell, and Christopher H. Eskiw. "Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts." Royal Society Open Science 2, no. 9 (2015): 150402. http://dx.doi.org/10.1098/rsos.150402.

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DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individu

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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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Tesi sul tema "Microarrays"

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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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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Marsden, David Michael. "3D small-molecule microarrays." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611660.

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Ooi, Siew Loon. "Yeast genetics of microarrays." Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3080738.

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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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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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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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Brunner, Thomas. "Designing oligonucleotides for DNA microarrays /." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Department of Computer Science, 2003. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=116.

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Hartmann, Michael. "Microfluidic Methods for Protein Microarrays." Doctoral thesis, KTH, Analytisk kemi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-26083.

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Protein microarray technology has an enormous potential for in vitro diagnostics (IVD)1. Miniaturized and parallelized immunoassays are powerful tools to measure dozens of parameters from minute amounts of sample, whilst only requiring small amounts of reagent. Protein microarrays have become well-established research tools in basic and applied research and the first diagnostic products are already released on the market. However, in order for protein microarrays to become broadly accepted tools in IVD, a number of criteria have to be fulfilled concerning robustness and automation. Robustness

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Taylor, Michael. "Surface analysis of polymer microarrays." Thesis, University of Nottingham, 2009. http://eprints.nottingham.ac.uk/10717/.

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Polymers have been used as biomaterials for nearly a century and have recently become the material of choice for use in tissue engineering. However, the classes of biodegradable and biocompatible polymers available for use in biomedical devices and as tissue engineering scaffolds are limited. This lack of available polymers with suitable properties could inhibit the development of biomedical devices with improved biocompatibility and hinder the growth of the fledgling tissue engineering field. Researchers in the polymer and biomaterials fields have tried to remedy this problem by applying comb

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Più fonti

Libri sul tema "Microarrays"

Jang, Rampal B. Microarrays. Humana Press, 2007. http://dx.doi.org/10.1385/159745303x.

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Dill, Kilian, Robin Hui Liu, and Piotr Grodzinski, eds. Microarrays. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-72719-6.

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Rampal, Jang B., ed. Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5.

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Rampal, Jang B., ed. Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-304-2.

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Muller, Hans-Joachim. Microarrays. Elsevier Academic Press, 2005.

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B, Rampal Jang, and Rampal Jang B, eds. Microarrays. 2nd ed. Humana, 2007.

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Thomas, Roeder, ed. Microarrays. Elsevier Academic Press, 2006.

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Kilcoyne, Michelle, and Jared Q. Gerlach, eds. Glycan Microarrays. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2148-6.

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Cretich, Marina, and Alessandro Gori, eds. Peptide Microarrays. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2732-7.

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Khademhosseini, Ali, Kahp-Yang Suh, and Mohammed Zourob, eds. Biological Microarrays. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-59745-551-0.

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Capitoli di libri sul tema "Microarrays"

Seliger, Hartmut. "Introduction." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_1.

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Chou, Cheng-Chung, and Konan Peck. "Design and Fabrication of Spotted Long Oligonucleotide Microarrays for Gene Expression Analysis." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_10.

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Rampal, Jang B., Peter J. Coassin, and Robert S. Matson. "Construction of In Situ Oligonucleotide Arrays on Plastic." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_11.

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Berry, Ian R., Carol A. Delaney, and Graham R. Taylor. "Detecting Ligated Fragments on Oligonucleotide Microarrays." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_12.

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Ho-Pun-Cheung, Alexandre, Hafid Abaibou, Philippe Cleuziat, and Evelyne Lopez-Crapez. "Detection of Single-Nucleotide Polymorphisms in Cancer-Related Genes by Minisequencing on a Microelectronic DNA Chip." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_13.

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Matson, Robert S., and Jang B. Rampal. "Hybridization Analysis Using Oligonucleotide Probe Arrays." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_14.

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Antohe, Bogdan V., and Patrick W. Cooley. "In Situ Synthesis of Peptide Microarrays Using Ink-Jet Microdispensing." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_15.

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Xu, Ming-Qun, Inca Ghosh, Samvel Kochinyan, and Luo Sun. "Intein-Mediated Peptide Arrays for Epitope Mapping and Kinase/Phosphatase Assays." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_16.

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Matson, Robert S., Raymond C. Milton, Michael C. Cress, Tom S. Chan, and Jang B. Rampal. "Printing Low Density Protein Arrays in Microplates." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_17.

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Zong, Yaping, Shanshan Zhang, Huang-Tsu Chen, Yunfei Zong, and Yaxian Shi. "Forward-Phase and Reverse-Phase Protein Microarray." In Microarrays. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_18.

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Atti di convegni sul tema "Microarrays"

Selcuk, Sahan Yoruc, Xilin Yang, Bijie Bai, et al. "Deep Learning-Enabled Classification of HER2 Score in Breast Cancer Using Pyramid Sampling." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.ath1b.7.

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We present an automated, deep learning-based method for HER2 score classification in breast cancer, achieving 85.47% accuracy on tissue microarrays from 300 patients. This method can significantly improve the HER2 evaluation process, saving diagnostician time.

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Tsai, Chih-Yuan, Hao-Sung Chiu, Chen-Hsun Wu, et al. "Color-conversion Arrays with Ultra-small Pixel Sizes for Micro-LED Displays." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jtu4a.60.

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We address mass transfer challenges using color-conversion technology, achieving ultrahigh-density microarrays: 11548 PPI for single color, 5774 PPI for full color, 72.98% and 65.36% conversion efficiencies for green and red, respectively, at 1.4-μm pixel size.

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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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Zien, Alexander, Juliane Fluck, Ralf Zimmer, and Thomas Lengauer. "Microarrays." In the sixth annual international conference. ACM Press, 2002. http://dx.doi.org/10.1145/565196.565239.

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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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Dawson, Elliott P., James Hudson, John Steward, Philip A. Donnell, Wing W. Chan, and Richard F. Taylor. "Membrane-based microarrays." In Photonics East '99, edited by Stephanus Buettgenbach. SPIE, 1999. http://dx.doi.org/10.1117/12.370292.

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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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Altug, Hatice. "Plasmonic Microarrays for Biology." In Bio-Optics: Design and Application. OSA, 2013. http://dx.doi.org/10.1364/boda.2013.bw3a.2.

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ESCANDE, DENIS G. "DNA MICROARRAYS AND ARRHYTHMIAS." In Proceedings of the 31st International Congress on Electrocardiology. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812702234_0079.

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Carlon, Enrico. "Thermodynamics of DNA microarrays." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-13.

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Rapporti di organizzazioni sul tema "Microarrays"

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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Rimm, David L. Spectral Analysis of Breast Cancer on Tissue Microarrays: Seeing Beyond Morphology. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417663.

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Gottardo, Raphael, Adrian E. Raftery, Ka Y. Yeung, and Roger E. Bumgarner. Bayesian Robust Inference for Differential Gene Expression in cDNA Microarrays with Multiple Samples. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada478418.

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Ljungman, Mats. Use of Nascent RNA Microarrays to Study Inducible Gene Expression in Breast Cancer Cells. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada443027.

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Cindy, Shi. Development of Microarrays-Based Metagenomics Technology for Monitoring Sulfate-Reducing Bacteria in Subsurface Environments. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1194725.

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Rimm, David L. Outcome Based Screening for Prognostic Phospho-RTK (Receptor Tyrosine Kinase) Antibodies Using Tissue Microarrays. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada410085.

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Rimm, David. Outcome Based Screening for Prognostic Phospho-RTK (Receptor Tyrosine Kinase) Antibodies Using Tissue Microarrays). Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430123.

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