Relevant bibliographies by topics / DNA microarrays gene expression

Academic literature on the topic 'DNA microarrays gene expression'

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Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "DNA microarrays gene expression"

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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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Harrington, Christina A., Carsten Rosenow, and Jacques Retief. "Monitoring gene expression using DNA microarrays." Current Opinion in Microbiology 3, no. 3 (June 2000): 285–91. http://dx.doi.org/10.1016/s1369-5274(00)00091-6.

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Wu, Shu-Hsing, Katrina Ramonell, Jeremy Gollub, and Shauna Somerville. "Plant gene expression profiling with DNA microarrays." Plant Physiology and Biochemistry 39, no. 11 (November 2001): 917–26. http://dx.doi.org/10.1016/s0981-9428(01)01322-5.

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King, Hadley C., and Animesh A. Sinha. "Gene Expression Profile Analysis by DNA Microarrays." JAMA 286, no. 18 (November 14, 2001): 2280. http://dx.doi.org/10.1001/jama.286.18.2280.

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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.
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Chinnaivan, Kavitha M., Arun Sreekumar, Walter M. Whitehouse, Naresh T. Gunaratnam, Stuart A. Winston, and Arul M. Chinnaiyan. "Profiling gene expression in atherosclerosis using DNA microarrays." Journal of the American College of Cardiology 39 (March 2002): 233. http://dx.doi.org/10.1016/s0735-1097(02)81038-5.

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Weindruch, Richard, Tsuyoshi Kayo, Cheol-Koo Lee, and Tomas A. Prolla. "Gene expression profiling of aging using DNA microarrays." Mechanisms of Ageing and Development 123, no. 2-3 (January 2002): 177–93. http://dx.doi.org/10.1016/s0047-6374(01)00344-x.

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Haviv, Izhak, and Ian G. Campbell. "DNA microarrays for assessing ovarian cancer gene expression." Molecular and Cellular Endocrinology 191, no. 1 (May 2002): 121–26. http://dx.doi.org/10.1016/s0303-7207(02)00063-1.

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Landgraf, Jeff, Ellen Wisman, Robert Schaffer, Monica Accerbi, Vernadette Simon, Matt Larson, and Pam Green. "Gene expression profiling in Arabidopsis using DNA microarrays." Nature Genetics 23, S3 (November 1999): 56. http://dx.doi.org/10.1038/14344.

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Arcellana-Panlilio, Mayi, and Stephen M. Robbins. "I. Global gene expression profiling using DNA microarrays." American Journal of Physiology-Gastrointestinal and Liver Physiology 282, no. 3 (March 1, 2002): G397—G402. http://dx.doi.org/10.1152/ajpgi.00519.2001.

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Having the complete human genomic sequence poses a new challenge: to use genomic structural information to display and analyze biological processes on a genome-wide scale to assign gene function. DNA microarrays are a miniaturized, ordered arrangement of nucleic acid fragments from individual genes located at defined positions on a solid support, enabling the analysis of thousands of genes in parallel by specific hybridization. This review describes technical aspects, discusses relevant applications, and suggests factors affecting the use of this technology and how it fits in the grand scheme of meeting the needs of the postgenomic era.
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Dissertations / Theses on the topic "DNA microarrays gene expression"

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Szeto, Lap Keung. "Clustering analysis of microarray gene expression data /." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-it-b19885817a.pdf.

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Thesis (M.Phil.)--City University of Hong Kong, 2005.
"Submitted to Department of Computer Engineering and Information Technology in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 70-79)
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Pendleton, Carly R. "A simulation-based approach for evaluating gene expression analyses /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1753.pdf.

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Alleyne, Renikko. "Tool for the identification of differentially expressed genes using a user-defined threshold /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2363.

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Gelmi, Claudio A. "A novel probabilistic framework for microarray data analysis from fundamental probability models to experimental validation /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 177 p, 2007. http://proquest.umi.com/pqdweb?did=1257806411&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Au, Siu Kie. "Gene expression profiling of non-small cell lung cancer using cDNA microarrays /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-bch-b23749490f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.
"Submitted to Department of Biology and Chemistry in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 133-147)
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Tjaden, Brian C. "Computational methods for transcription anlysis using oligonucleotide microarrays /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/6907.

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Jiang, Ying, and 蔣穎. "Studies of gene regulation using microarray data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29976388.

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Srivastava, Gyan Prakash Xu Dong. "Genome scale meta analysis of microarrays for biological inferences." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6841.

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Title from PDF of title page (University of Missouri--Columbia, viewed on April 5, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Dong Xu. Vita. Includes bibliographical references.
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Kirk, Michael School of Biotechnology &amp Biomolecular Science UNSW. "Bioinformatic analyses of microarray experiments on genetic control of gene expression level." Awarded by:University of New South Wales. School of Biotechnology and Biomolecular Science, 2006. http://handle.unsw.edu.au/1959.4/25986.

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The advent of microarray technology, allowing measurement of gene expression levels for thousands of genes in parallel, has made possible experiments designed to investigate the genetic control of variation in gene expression level (described in the literature as ???genetical genomics??? or ???eQTL??? experiments). Published results from these studies, in yeast and in mice, show that genetic variation is an important factor in gene regulation, and furthermore that individual polymorphisms modify the expression level of many genes. The concern of this thesis is the bioinformatic analyses of the expression level and genotype data sets that are the raw material for these studies. In particular this thesis addresses the two issues of detection of artefactual effects, and maximizing the information that can be extracted from the data. It is shown that while a polymorphism affecting the expression of many genes may be readily detected, care must be taken to determine whether the detected effect is genuinely one of genetic control of expression level, rather than the effect of correlations in measured expression level not of genetic cause. A significance test is devised to distinguish between these cases. The detection of artefactual correlation is explored further in the reanalysis of the published data from a large yeast study. A critique is given of the permutation method used to ascribe genetic control as the cause of inter gene expression level correlation. The presence of some degree of artefactual correlation is shown, and novel methods are presented for identifying such artefacts. To extend the analyses that may be applied to eQTL data, an algorithm is presented for determining secondary eQTLs for gene expression level (as opposed to a single primary QTL), along with a significance test for the putative QTL found. The technique is demonstrated on a large public data set. In addition to the use for which they are intended, the data sets generated for eQTL studies provide opportunities for additional analyses. In this thesis a method is developed for calculating a genome wide map of meiotic recombination frequency from the genotype data for multiple segregant strains. The method is demonstrated on the published genotype data generated for a large yeast eQTL study.
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Kannan, Anusha Aiyalu. "Detecting relevant changes in high throughput gene expression data /." Online version of thesis, 2008. http://hdl.handle.net/1850/10832.

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Books on the topic "DNA microarrays gene expression"

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1940-, Hatfield G. Wesley, ed. DNA microarrays and gene expression. New York: Cambridge University Press, 2002.

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Jordan, B. R., ed. DNA Microarrays: Gene Expression Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5.

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Göhlmann, Hinrich. Gene expression studies using affymetrix microarrays. Boca Raton: Taylor & Francis, 2009.

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

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Thomas, Roeder, ed. Microarrays. Burlington, MA: Elsevier Academic Press, 2006.

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Muller, Hans-Joachim. Microarrays. Burlington, MA: Elsevier Academic Press, 2005.

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John, Quackenbush, and Brazma Alvis, eds. Microarray gene expressions data analysis: A beginner's guide. Oxford: Blackwell, 2003.

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Baldi, Pierre. DNA microarrays and gene expression: From experiments to data analysis and modeling. Cambridge, U.K: Cambridge University Press, 2002.

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Cancer gene profiling: Methods and protocols. New York, NY: Humana Press, 2010.

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Microarray analysis. Hoboken, NJ: Wiley-Liss, 2003.

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Book chapters on the topic "DNA microarrays gene expression"

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Jordan, Bertrand R. "DNA Arrays for Expression Measurement: An Historical Perspective." In DNA Microarrays: Gene Expression Applications, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_1.

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Grimmond, Sean, and Andy Greenfield. "Expression Profiling with cDNA Microarrays: A User’s Perspective and Guide." In DNA Microarrays: Gene Expression Applications, 13–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_2.

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Peck, Konan, and Yuh-Pyng Sher. "cDNA Microarrays on Nylon Membranes with Enzyme Colorimetric Detection." In DNA Microarrays: Gene Expression Applications, 35–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_3.

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Loriod, Béatrice, Geneviève Victorero, and Catherine Nguyen. "cDNA Macroarrays and Microarrays on Nylon Membranes with Radioactive Detection." In DNA Microarrays: Gene Expression Applications, 55–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_4.

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Casellas, Pierre, Annick Peleraux, and Sylvaine Galiegue. "Oligonucleotide Chips for Expression Analysis: Principles and Practical Procedures." In DNA Microarrays: Gene Expression Applications, 85–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_5.

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Brazma, Alvis, Alan Robinson, and Jaak Vilo. "Gene Expression Data Mining and Analysis." In DNA Microarrays: Gene Expression Applications, 105–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_6.

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Jordan, Bertrand R. "Future Trends in the Use of DNA Arrays for Expression Measurement." In DNA Microarrays: Gene Expression Applications, 131–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_7.

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Maruyama, Kyonoshin, Kazuko Yamaguchi-Shinozaki, and Kazuo Shinozaki. "Gene Expression Profiling Using DNA Microarrays." In Methods in Molecular Biology, 381–91. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-580-4_20.

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Bortoli, Michele, and Nicoletta Biglia. "Gene Expression Profiling with DNA Microarrays." In Biomarkers in Breast Cancer, 47–61. Totowa, NJ: Humana Press, 2006. http://dx.doi.org/10.1385/1-59259-915-x:047.

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Stangegaard, Michael. "Gene Expression Analysis Using Agilent DNA Microarrays." In Methods in Molecular Biology, 133–45. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-538-1_9.

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Conference papers on the topic "DNA microarrays gene expression"

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Benso, A., S. Di Carlo, G. Politano, and L. Sterpone. "A graph-based representation of Gene Expression profiles in DNA microarrays." In 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2008). IEEE, 2008. http://dx.doi.org/10.1109/cibcb.2008.4675762.

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Benso, Alfredo, Stefano Di Carlo, Gianfranco Politano, and Luca Sterpone. "Differential gene expression graphs: A data structure for classification in DNA microarrays." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696689.

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Zhao, Hongya, Qing-Hua Huang, Kwok Leung Chan, Lee-Ming Cheng, and Hong Yan. "Mining functional biclusters of DNA microarray gene expression data." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811539.

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Wu, Shuanhu, Hong Yan, Qingshang Zeng, Yanjie Zhang, and Yibin Song. "Constrained Competitive Learning Algorithm for DNA Microarray Gene Expression Data Analysis." In 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2008. http://dx.doi.org/10.1109/icicta.2008.7.

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Shi, Jinlong, and Zhigang Luo. "Missing value estimation for DNA microarray gene expression data with principal curves." In 2010 International Conference on Bioinformatics and Biomedical Technology. IEEE, 2010. http://dx.doi.org/10.1109/icbbt.2010.5478964.

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Kong, Wei, Xiaoyang Mou, and Bin Yang. "Study DNA Microarray Gene Expression Data of Alzheimer's Disease by Independent Component Analysis." In 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing. IEEE, 2009. http://dx.doi.org/10.1109/ijcbs.2009.106.

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Liew, Alan Wee-Chung, and Hong Yan. "Reliable detection of short periodic gene expression time series profiles in DNA microarray data." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346812.

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Dykema, Karl J., Zeng Quan Yang, Kyle A. Furge, Stephen P. Ethier, and Ramsi Haddad. "Abstract 117: jackTom, an algorithm to integrate aCGH and gene expression microarrays to predict activation of oncogenes by DNA amplification in individual tumors." In Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/1538-7445.am10-117.

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Saha, Sujay, Saikat Bandopadhyay, Anupam Ghosh, and Kashi Nath Dey. "An improved fuzzy based approach to impute missing values in DNA microarray gene expression data with collaborative filtering." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732161.

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Salem, Hanaa, Gamal Attiya, and Nawal El-Fishawy. "Multi-Agent based Intelligent Decision Support Systems for Cancer Classification." In Agria Média 2020 : „Az oktatás digitális átállása korunk pedagógiai forradalma”. Eszterházy Károly Egyetem Líceum Kiadó, 2021. http://dx.doi.org/10.17048/am.2020.122.

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There is evidence that early detection of cancer diseases can improve the treatment and increase the survival rate of patients. This paper presents an efficient CAD system for cancer diseases diagnosis by gene expression profiles of DNA microarray datasets. The proposed CAD system combines Intelligent Decision Support System (IDSS) and Multi-Agent (MA) system. The IDSS represents the backbone of the entire CAD system. It consists of two main phases; feature selection/reduction phase and a classification phase. In the feature selection/reduction phase, eight diverse methods are developed. While, in the classification phase, three evolutionary machine learning algorithms are employed. On the other hand, the MA system manages the entire operation of the CAD system. It first initializes several IDSSs (exactly 24 IDSSs) with the aid of mobile agents and then directs the generated IDSSs to run concurrently on the input dataset. Finally, a master agent selects the best classification, as the final report, based on the best classification accuracy returned from the 24 IDSSs The proposed CAD system is implemented in JAVA, and evaluated by using three microarray datasets including; Leukemia, Colon tumor, and Lung cancer. The system is able to classify different types of cancer diseases accurately in a very short time. This is because the MA system invokes 24 different IDSS to classify the diseases concurrently in parallel processing manner before taking the decision of the best classification result.
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Reports on the topic "DNA microarrays gene expression"

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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. Fort Belvoir, VA: Defense Technical Information Center, July 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. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada443027.

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Chang-Liu, Chin-Mei, and G. E. Wolschak. Effect of passage number on cellular response DNA-damaging agents: cell survival and gene expression. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/206623.

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Chang-Liu, C. M., and G. E. Woloschak. Effect of passage number on cellular response to DNA-damaging agents: Cell survival and gene expression. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/515535.

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Libertin, C. R., P. Weaver, G. E. Woloschak, and S. Mobarhan. Subnormal albumin gene expression is associated with weight loss in immunodeficient/DNA-repair-deficient wasted mice. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10184609.

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Shook, Molly S., Elena N. Cravens, Erika J. Hughes, Scott A. Coonrod, and Eric J. Richards. ICBP90 Regulation of DNA Methylation, Histone Ubiquitination, and Tumor Suppressor Gene Expression in Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada566787.

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Shook, Molly. ICBP90 Regulation of DNA Methylation, Histone Ubiquitination, and Tumor Suppressor Gene Expression in Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada551855.

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Ye, Shanli. DNA Sequences Involved in the Regulation of Human c-myc Gene Expression by Herpes Simplex Virus Type 1 (HSV-1). Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7097.

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Beachy, R. N. [Genetic engineering with a gene encoding a soybean storage protein to identify DNA sequences to control its expression]: Annual report, 1993. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10133802.

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Christman, Judith K. Role of DNA Methylation in Altering Gene Expression During the Early Stages of Human Breast Cancer Progression in the MCF10AT Xenograft Model. Fort Belvoir, VA: Defense Technical Information Center, April 2003. http://dx.doi.org/10.21236/ada418564.

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