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1

Weitzman, Jonathan B. "DNA/DNA microarrays." Genome Biology 2 (2001): spotlight—20010813–03. http://dx.doi.org/10.1186/gb-spotlight-20010813-03.

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2

Hofman, Paul. "DNA Microarrays." Nephron Physiology 99, no. 3 (February 24, 2005): p85—p89. http://dx.doi.org/10.1159/000083764.

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3

Cook, Stuart A., and Anthony Rosenzweig. "DNA Microarrays." Circulation Research 91, no. 7 (October 4, 2002): 559–64. http://dx.doi.org/10.1161/01.res.0000036019.55901.62.

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4

Niemeyer, Christof M., and Dietmar Blohm. "DNA Microarrays." Angewandte Chemie International Edition 38, no. 19 (October 4, 1999): 2865–69. http://dx.doi.org/10.1002/(sici)1521-3773(19991004)38:19<2865::aid-anie2865>3.0.co;2-f.

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5

Biesen, R., and T. Häupl. "DNA-Microarrays." Zeitschrift für Rheumatologie 70, no. 9 (September 30, 2011): 803–8. http://dx.doi.org/10.1007/s00393-011-0869-4.

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6

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.
7

Peiffer, Daniel, Ken Cho, and Yongchol Shin. "Xenopus DNA Microarrays." Current Genomics 4, no. 8 (November 1, 2003): 665–72. http://dx.doi.org/10.2174/1389202033490097.

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8

Schofield, W. C. E., J. McGettrick, T. J. Bradley, J. P. S. Badyal, and S. Przyborski. "Rewritable DNA Microarrays." Journal of the American Chemical Society 128, no. 7 (February 2006): 2280–85. http://dx.doi.org/10.1021/ja056367r.

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9

HENRY, CELIA M. "STANDARDIZING DNA MICROARRAYS." Chemical & Engineering News 82, no. 31 (August 2, 2004): 36–39. http://dx.doi.org/10.1021/cen-v082n031.p036.

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10

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.
11

Jack, Philippa, and David Boyle. "DNA microarrays for pathogen detection and characterisation." Microbiology Australia 27, no. 2 (2006): 68. http://dx.doi.org/10.1071/ma06068.

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DNA microarrays have three main potential diagnostic uses in clinical microbiology: detection of known pathogens, pathogen typing and novel pathogen discovery. Although DNA microarray platforms offer the ability to screen for a large number of agents in parallel, sensitivity is dependent on the ability to obtain adequate amounts of pathogen nucleic acids from collected samples. In general, high levels of sensitivity require a PCR amplification step using specific primer sets, subsequently reducing the overall scope of the microarray assay. At present, relatively high costs, restricted sample throughput capabilities and validation difficulties are also major factors limiting the implementation of DNA microarray assays in diagnostic microbiology laboratories.
12

Chagovetz, Alexander, and Steve Blair. "Real-time DNA microarrays: reality check." Biochemical Society Transactions 37, no. 2 (March 20, 2009): 471–75. http://dx.doi.org/10.1042/bst0370471.

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DNA microarrays are plagued with inconsistent quantifications and false-positive results. Using established mechanisms of surface reactions, we argue that these problems are inherent to the current technology. In particular, the problem of multiplex non-equilibrium reactions cannot be resolved within the framework of the existing paradigm. We discuss the advantages and limitations of changing the paradigm to real-time data acquisition similar to real-time PCR methodology. Our analysis suggests that the fundamental problem of multiplex reactions is not resolved by the real-time approach itself. However, by introducing new detection chemistries and analysis approaches, it is possible to extract target-specific quantitative information from real-time microarray data. The possible scope of applications for real-time microarrays is discussed.
13

Call, Douglas R., Marlene K. Bakko, Melissa J. Krug, and Marilyn C. Roberts. "Identifying Antimicrobial Resistance Genes with DNA Microarrays." Antimicrobial Agents and Chemotherapy 47, no. 10 (October 2003): 3290–95. http://dx.doi.org/10.1128/aac.47.10.3290-3295.2003.

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ABSTRACT We developed and tested a glass-based microarray suitable for detecting multiple tetracycline (tet) resistance genes. Microarray probes for 17 tet genes, the β-lactamase bla TEM-1 gene, and a 16S ribosomal DNA gene (Escherichia coli) were generated from known controls by PCR. The resulting products (ca. 550 bp) were applied as spots onto epoxy-silane-derivatized, Teflon-masked slides by using a robotic spotter. DNA was extracted from test strains, biotinylated, hybridized overnight to individual microarrays at 65°C, and detected with Tyramide Signal Amplification, Alexa Fluor 546, and a microarray scanner. Using a detection threshold of 3× the standard deviation, we correctly identified tet genes carried by 39 test strains. Nine additional strains were not known to harbor any genes represented on the microarray, and these strains were negative for all 17 tet probes as expected. We verified that R741a, which was originally thought to carry a novel tet gene, tet(I), actually harbored a tet(G) gene. Microarray technology has the potential for screening a large number of different antibiotic resistance genes by the relatively low-cost methods outlined in this paper.
14

Stoevesandt, O., M. He, and M. J. Taussig. "Repeatable printing of protein microarrays from DNA microarrays." New Biotechnology 25 (September 2009): S360. http://dx.doi.org/10.1016/j.nbt.2009.06.961.

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15

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.
16

Herrera, Henry J., and Marlon Gancino. "DNA microarrays: Recent Advances." Bionatura 2, no. 3 (August 15, 2017): 404–6. http://dx.doi.org/10.21931/rb/2017.02.03.13.

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17

Dai, Wei, Mona A. Sheikh, Olgica Milenkovic, and Richard G. Baraniuk. "Compressive Sensing DNA Microarrays." EURASIP Journal on Bioinformatics and Systems Biology 2009 (2009): 1–12. http://dx.doi.org/10.1155/2009/162824.

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18

Meloni, R. "DNA microarrays and pharmacogenomics." Pharmacological Research 49, no. 4 (April 2004): 303–8. http://dx.doi.org/10.1016/j.phrs.2003.06.001.

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19

Sassolas, Audrey, Béatrice D. Leca-Bouvier, and Loïc J. Blum. "DNA Biosensors and Microarrays." Chemical Reviews 108, no. 1 (January 2008): 109–39. http://dx.doi.org/10.1021/cr0684467.

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20

Rathod, Pradipsinh K., Karthikeyan Ganesan, Rhian E. Hayward, Zbynek Bozdech, and Joseph L. DeRisi. "DNA microarrays for malaria." Trends in Parasitology 18, no. 1 (January 2002): 39–45. http://dx.doi.org/10.1016/s1471-4922(01)02153-5.

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21

Marcotte, Eric R., Lalit K. Srivastava, and Rémi Quirion. "DNA microarrays in neuropsychopharmacology." Trends in Pharmacological Sciences 22, no. 8 (August 2001): 426–36. http://dx.doi.org/10.1016/s0165-6147(00)01741-7.

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22

Smith, L. "DNA microarrays and development." Human Molecular Genetics 12, no. 90001 (April 2, 2003): 1R—8. http://dx.doi.org/10.1093/hmg/ddg053.

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23

Kostrzynska, M., and A. Bachand. "Application of DNA microarray technology for detection, identification, and characterization of food-borne pathogens." Canadian Journal of Microbiology 52, no. 1 (January 1, 2006): 1–8. http://dx.doi.org/10.1139/w05-105.

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DNA microarrays represent the latest advance in molecular technology. In combination with bioinformatics, they provide unparalleled opportunities for simultaneous detection of thousands of genes or target DNA sequences and offer tremendous potential for studying food-borne microorganisms. This review provides an up-to-date look at the application of DNA microarray technology to detect food-borne pathogenic bacteria, viruses, and parasites. In addition, it covers the advantages of using microarray technology to further characterize microorganisms by providing information for specific identification of isolates, to understand the pathogenesis based on the presence of virulence genes, and to indicate how new pathogenic strains evolved epidemiologically and phylogenetically.Key words: DNA microarrays, food-borne pathogens, detection.
24

I. A., Zaloilo. "APPLYING OF DNA- MICROARRAYS IN A MODERN FISH-FARMING." Biotechnologia Acta 8, no. 4 (August 2015): 9–20. http://dx.doi.org/10.15407/biotech8.04.009.

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25

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 (September 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 individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data.
26

&NA;. "DNA microarrays in clinical practice." Inpharma Weekly &NA;, no. 1306 (September 2001): 3. http://dx.doi.org/10.2165/00128413-200113060-00004.

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27

Akin, H. E., D. A. O. Karabay, J. R. Kyle, A. P. Mills, C. S. Ozkan, and M. Ozkan. "Electronic Microarrays in DNA Computing." Journal of Nanoscience and Nanotechnology 11, no. 3 (March 1, 2011): 1859–65. http://dx.doi.org/10.1166/jnn.2011.3422.

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28

Akin, H. E., D. A. O. Karabay, J. R. Kyle, A. P. Mills, Jr., C. Ozkan, and M. Ozkan. "Electronic Microarrays in DNA Computing." Journal of Nanoscience and Nanotechnology 11, no. 6 (June 1, 2011): 4717–23. http://dx.doi.org/10.1166/jnn.2011.38844717.

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29

Booth, David E. "Cancer Diagnostics With DNA Microarrays." Technometrics 49, no. 4 (November 2007): 492–93. http://dx.doi.org/10.1198/tech.2007.s686.

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30

Chow, B. Y., C. J. Emig, and J. M. Jacobson. "Photoelectrochemical synthesis of DNA microarrays." Proceedings of the National Academy of Sciences 106, no. 36 (August 21, 2009): 15219–24. http://dx.doi.org/10.1073/pnas.0813011106.

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31

Russell, S. "DNA Microarrays: Gene Expression Applications." Heredity 89, no. 5 (October 28, 2002): 402. http://dx.doi.org/10.1038/sj.hdy.6800150.

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32

BURLEIGH, B. A. "ProbingTrypanosoma cruzibiology with DNA microarrays." Parasitology 128, S1 (October 2004): S3—S10. http://dx.doi.org/10.1017/s0031182004006559.

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The application of genome-scale approaches to studyTrypanosoma cruzi–host interactions at different stages of the infective process is becoming possible with sequencing and assembly of theT. cruzigenome nearing completion and sequence information available for both human and mouse genomes. Investigators have recently begun to exploit DNA microarray technology to analyze host transcriptional responses toT. cruziinfection and dissect developmental processes in the complexT. cruzilife-cycle. Collectively, information generated from these and future studies will provide valuable insights into the molecular requirements for establishment ofT. cruziinfection in the host and highlight the molecular events coinciding with disease progression. While the field is in its infancy, the availability of genomic information and increased accessibility to relatively high-throughput technologies represents a significant advancement toward identification of novel drug targets and vaccine candidates for the treatment and prevention of Chagas' disease.
33

Hughes, Timothy R., and Daniel D. Shoemaker. "DNA microarrays for expression profiling." Current Opinion in Chemical Biology 5, no. 1 (February 2001): 21–25. http://dx.doi.org/10.1016/s1367-5931(00)00163-0.

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34

van Berkum, Nynke L., and Frank CP Holstege. "DNA microarrays: raising the profile." Current Opinion in Biotechnology 12, no. 1 (February 2001): 48–52. http://dx.doi.org/10.1016/s0958-1669(00)00173-7.

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35

Conzone, Samuel D., and Carlo G. Pantano. "Glass slides to DNA microarrays." Materials Today 7, no. 3 (March 2004): 20–26. http://dx.doi.org/10.1016/s1369-7021(04)00122-1.

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36

Wooster, Richard. "Cancer classification with DNA microarrays." Trends in Genetics 16, no. 8 (August 2000): 327–29. http://dx.doi.org/10.1016/s0168-9525(00)02064-3.

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37

Shoemaker, Daniel D., and Peter S. Linsley. "Recent developments in DNA microarrays." Current Opinion in Microbiology 5, no. 3 (June 2002): 334–37. http://dx.doi.org/10.1016/s1369-5274(02)00327-2.

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38

Reymond, Philippe. "DNA microarrays and plant defence." Plant Physiology and Biochemistry 39, no. 3-4 (March 2001): 313–21. http://dx.doi.org/10.1016/s0981-9428(00)01235-3.

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39

Henry, Celia M. "Focus: DNA microarrays in toxicology." Analytical Chemistry 71, no. 13 (July 1999): 462A—464A. http://dx.doi.org/10.1021/ac990494m.

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40

Yasokawa, Daisuke, and Hitoshi Iwahashi. "Toxicogenomics using yeast DNA microarrays." Journal of Bioscience and Bioengineering 110, no. 5 (November 2010): 511–22. http://dx.doi.org/10.1016/j.jbiosc.2010.06.003.

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41

P, Deepthi, and C. H. Renumadhavi. "DNA Microarrays and Smart Pooling." IOSR Journal of Pharmacy and Biological Sciences 9, no. 1 (2014): 61–64. http://dx.doi.org/10.9790/3008-09136164.

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42

Kim, C., M. Li, A. Lowe, N. Venkataramaiah, K. Richmond, J. Kaysen, and F. Cerrina. "DNA microarrays: An imaging study." Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures 21, no. 6 (2003): 2946. http://dx.doi.org/10.1116/1.1627802.

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43

Ramaswamy, Sridhar, and Todd R. Golub. "DNA Microarrays in Clinical Oncology." Journal of Clinical Oncology 20, no. 7 (April 1, 2002): 1932–41. http://dx.doi.org/10.1200/jco.2002.20.7.1932.

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ABSTRACT: Aberrant gene expression is critical for tumor initiation and progression. However, we lack a comprehensive understanding of all genes that are aberrantly expressed in human cancer. Recently, DNA microarrays have been used to obtain global views of human cancer gene expression and to identify genetic markers that might be important for diagnosis and therapy. We review clinical applications of these novel tools, discuss some important recent studies, identify promising avenues of research in this emerging field of study, and discuss the likely impact that expression profiling will have on clinical oncology.
44

Kochzius, M., M. Nölte, H. Weber, N. Silkenbeumer, S. Hjörleifsdottir, G. O. Hreggvidsson, V. Marteinsson, et al. "DNA Microarrays for Identifying Fishes." Marine Biotechnology 10, no. 2 (February 13, 2008): 207–17. http://dx.doi.org/10.1007/s10126-007-9068-3.

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45

Ho, Shuk-Mei, and Kin-Mang Lau. "DNA microarrays in prostate cancer." Current Urology Reports 3, no. 1 (February 2002): 53–60. http://dx.doi.org/10.1007/s11934-002-0011-x.

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46

Horváth, Szatmár, Zoltán Janka, and Károly Mirnics. "Analyzing Schizophrenia by DNA Microarrays." Biological Psychiatry 69, no. 2 (January 2011): 157–62. http://dx.doi.org/10.1016/j.biopsych.2010.07.017.

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47

Philippakis, Anthony A., Aaron M. Qureshi, Michael F. Berger, and Martha L. Bulyk. "Design of Compact, Universal DNA Microarrays for Protein Binding Microarray Experiments." Journal of Computational Biology 15, no. 7 (September 2008): 655–65. http://dx.doi.org/10.1089/cmb.2007.0114.

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48

Sui, Yunxia, Xiaoyue Zhao, Terence P. Speed, and Zhijin Wu. "Background Adjustment for DNA Microarrays Using a Database of Microarray Experiments." Journal of Computational Biology 16, no. 11 (November 2009): 1501–15. http://dx.doi.org/10.1089/cmb.2009.0063.

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49

Lacroix, M., N. Zammatteo, J. Remacle, and G. Leclercq. "A Low-Density DNA Microarray for Analysis of Markers in Breast Cancer." International Journal of Biological Markers 17, no. 1 (January 2002): 5–23. http://dx.doi.org/10.1177/172460080201700102.

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Breast cancer remains a major cause of death in women from Western countries. In the near future, advances in both nucleic acids technology and tumor biology should be widely exploited to improve the diagnosis, prognosis, and outcome prediction of this disease. The DNA microarray, also called biochip, is a promising tool for performing massive, simultaneous, fast, and standardized analyses of multiple molecular markers in tumor samples. However, most currently available microarrays are expensive, which is mainly due to the amount (several thousands) of different DNA capture sequences that they carry. While these high-density microarrays are best suited for basic studies, their introduction into the clinical routine remains hypothetical. We describe here the principles of a low-density microarray, carrying only a few hundreds of capture sequences specific to markers whose importance in breast cancer is generally recognized or suggested by the current medical literature. We provide a list of about 250 of these markers. We also examine some potential difficulties (homologies between marker and/or variant sequences, size of sequences, etc.) associated with the production of such a low-cost microarray.
50

Widłak, Piotr. "DNA microarrays, a novel approach in studies of chromatin structure." Acta Biochimica Polonica 51, no. 1 (March 31, 2004): 1–8. http://dx.doi.org/10.18388/abp.2004_3592.

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The DNA microarray technology delivers an experimental tool that allows surveying expression of genetic information on a genome-wide scale at the level of single genes--for the new field termed functional genomics. Gene expression profiling--the primary application of DNA microarrays technology--generates monumental amounts of information concerning the functioning of genes, cells and organisms. However, the expression of genetic information is regulated by a number of factors that cannot be directly targeted by standard gene expression profiling. The genetic material of eukaryotic cells is packed into chromatin which provides the compaction and organization of DNA for replication, repair and recombination processes, and is the major epigenetic factor determining the expression of genetic information. Genomic DNA can be methylated and this modification modulates interactions with proteins which change the functional status of genes. Both chromatin structure and transcriptional activity are affected by the processes of replication, recombination and repair. Modified DNA microarray technology could be applied to genome-wide study of epigenetic factors and processes that modulate the expression of genetic information. Attempts to use DNA microarrays in studies of chromatin packing state, chromatin/DNA-binding protein distribution and DNA methylation pattern on a genome-wide scale are briefly reviewed in this paper.

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