Relevant bibliographies by topics / Gene expression analysis

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

Academic literature on the topic 'Gene expression analysis'

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

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Journal articles on the topic "Gene expression analysis"

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R, Dr Prema. "Feature Selection for Gene Expression Data Analysis – A Review." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 6955–64. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020695.

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Liu, Junjie, Peng Li, Liuyang Lu, Lanfen Xie, Xiling Chen, and Baizhong Zhang. "Selection and evaluation of potential reference genes for gene expression analysis in Avena fatua Linn." Plant Protection Science 55, No. 1 (2018): 61–71. http://dx.doi.org/10.17221/20/2018-pps.

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Eight commonly used candidate reference genes, 18S ribosomal RNA (rRNA) (18S), 28S rRNA (28S), actin (ACT), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), elongation factor 1 alpha (EF1α), ribosomal protein L7 (RPL7), Alpha-tubulin (α-TUB), and TATA box binding protein-associated factor (TBP), were evaluated under various experimental conditions to assess their suitability in different developmental stages, tissues and herbicide treatments in Avena fatua. The results indicated the most suitable reference genes for the different experimental conditions. For developmental stages, 28S and EF1α
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Anitha, S., and Dr C. P. Chandran. "Review on Analysis of Gene Expression Data Using Biclustering Approaches." Bonfring International Journal of Data Mining 6, no. 2 (2016): 16–23. http://dx.doi.org/10.9756/bijdm.8135.

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S, Kavya. "A Review on: Gene Expression Analysis Techniques and its Application." International Journal of Research Publication and Reviews 5, no. 4 (2024): 9928–33. http://dx.doi.org/10.55248/gengpi.5.0424.1145.

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YASUE, Hiroshi, Koji DOI, and Hideki HIRAIWA. "Gene Expression Analysis." Journal of Animal Genetics 48, no. 1 (2019): 9–18. http://dx.doi.org/10.5924/abgri.48.9.

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Oetting, William S. "Gene Expression Analysis." Pigment Cell Research 13, no. 1 (2000): 21–27. http://dx.doi.org/10.1034/j.1600-0749.2000.130105.x.

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Carvalho, Felicia I., Christopher Johns, and Marc E. Gillespie. "Gene expression analysis." Biochemistry and Molecular Biology Education 40, no. 3 (2012): 181–90. http://dx.doi.org/10.1002/bmb.20588.

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Winter, Holger, Kerstin Korn, and Rudolf Rigler. "Direct Gene Expression Analysis." Current Pharmaceutical Biotechnology 5, no. 2 (2004): 191–97. http://dx.doi.org/10.2174/1389201043376995.

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Stein,, Richard A. "Gene-Expression Analysis Redefined." Genetic Engineering & Biotechnology News 31, no. 7 (2011): 1–31. http://dx.doi.org/10.1089/gen.31.7.13.

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Kozian, D. "Comparative gene-expression analysis." Trends in Biotechnology 17, no. 2 (1999): 73–78. http://dx.doi.org/10.1016/s0167-7799(98)01292-x.

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Dissertations / Theses on the topic "Gene expression analysis"

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Curtis, R. K. "Control analysis of gene expression." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598230.

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This thesis describes the development of the application of modular regulation analysis, a subset of metabolic control analysis to microarray data. Microarray experiments measure complex changes in the abundance of many mRNAs under different conditions. Current analysis methods, such as clustering, cannot distinguish between direct and indirect effects on expression, or calculate the relative importance of mRNAs in effecting responses. Modular regulation analysis of microarray data reveals and quantifies which mRNA changes are important for cellular responses.  The mRNAs are clustered, then ho
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Johansson, Karin. "Analysis of immunoglobulin gene expression focus on Oct2 /." Lund : Dept. of Cell and Molecular Biology, Lund University, 1995. http://catalog.hathitrust.org/api/volumes/oclc/39776663.html.

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Liebermeister, Wolfram. "Analysis of optimal differential gene expression." Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=97257347X.

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Muthukaruppan, Anita. "Gene expression analysis in breast cancer." Thesis, University of Auckland, 2011. http://hdl.handle.net/2292/6997.

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Breast cancer is a leading cause of malignancy worldwide. Improvements to gene expression profiling technology have resulted in the identification of many prognostic and predictive gene expression signatures for breast cancer. Whilst some of these signatures are being developed commercially, only two prognostic signatures: MammaPrint and Oncotype DX, are currently being validated in clinical trials. Many of these gene expression signatures require independent validation and the underlying biology behind these signatures remains unclear. The aim of this thesis is to identify key molecular pathw
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Sohler, Florian. "Contextual Analysis of Gene Expression Data." Diss., lmu, 2006. http://nbn-resolving.de/urn:nbn:de:bvb:19-55936.

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Siangphoe, Umaporn. "META-ANALYSIS OF GENE EXPRESSION STUDIES." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/4040.

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Combining effect sizes from individual studies using random-effects models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistency of sample qualities and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. We proposed two new methods for random effects estimation and measurements for model variation and strength of the study heterogeneity. We then developed a statistical technique to test for significance of random effects and identify heterogeneous genes. We also propo
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Yeung, Ka Yee. "Cluster analysis of gene expression data /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6986.

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Boonjakuakul, Jenni Kim. "Analysis of Helicobacter pylori gene expression /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.

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Thamrin, Sri Astuti. "Bayesian survival analysis using gene expression." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62666/1/Sri_Astuti_Thamrin_Thesis.pdf.

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This thesis developed and applied Bayesian models for the analysis of survival data. The gene expression was considered as explanatory variables within the Bayesian survival model which can be considered the new contribution in the analysis of such data. The censoring factor that is inherent of survival data has also been addressed in terms of its impact on the fitting of a finite mixture of Weibull distribution with and without covariates. To investigate this, simulation study were carried out under several censoring percentages. Censoring percentage as high as 80% is acceptable here as the w
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Campbell, Lisa Jane. "Gene expression analysis of telomerase related genes in myeloid malignancy." Thesis, Open University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578282.

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Telomere shortening and an increased telomerase activity are associated with poor prognosis and disease progression in many cancers. In Chronic Myeloid Leukaemia (CML) telomere shortening has a strong correlation with disease progression. Expression of hTERT, the catalytic component of telomerase, was evaluated in the CD34+ cells of CML patients. This revealed that expression of hTERT was significantly reduced in chronic phase CML and decreased with disease progression to accelerated phase and blast crisis. .It could therefore be concluded that reduced hTERT expression contributes to reduced t
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Books on the topic "Gene expression analysis"

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Raghavachari, Nalini, and Natàlia Garcia-Reyero, eds. Gene Expression Analysis. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7834-2.

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Raghavachari, Nalini, and Natalia Garcia-Reyero, eds. Gene Expression Analysis. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4276-4.

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

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Nielsen, Kåre Lehmann, ed. Serial Analysis of Gene Expression (SAGE). Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-454-4.

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Parmigiani, Giovanni, Elizabeth S. Garrett, Rafael A. Irizarry, and Scott L. Zeger, eds. The Analysis of Gene Expression Data. Springer New York, 2003. http://dx.doi.org/10.1007/b97411.

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1970-, Gold David, and Baladandayuthapani Veerabhadran 1976-, eds. Bayesian analysis of gene expression data. Wiley, 2009.

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1965-, Mallick Bani K., Gold David 1970-, and Baladandayuthapani Veerabhadran 1976-, eds. Bayesian analysis of gene expression data. Wiley, 2009.

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J, Link Andrew, ed. 2-D proteome analysis protocols. Humana Press, 1999.

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

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Lee, Peter Daniel. Analysis of multidrug resistance gene expression in osteosarcoma. National Library of Canada, 1994.

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Book chapters on the topic "Gene expression analysis"

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Gondro, Cedric. "Gene Expression Analysis." In Use R! Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14475-7_5.

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Ittmann, Michael. "Gene Expression Analysis." In Molecular Pathology Library. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64096-9_11.

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Barah, Pankaj, Dhruba Kumar Bhattacharyya, and Jugal Kumar Kalita. "Differential Expression Analysis." In Gene Expression Data Analysis. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429322655-6.

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Barah, Pankaj, Dhruba Kumar Bhattacharyya, and Jugal Kumar Kalita. "Co-Expression Analysis." In Gene Expression Data Analysis. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429322655-5.

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Barah, Pankaj, Dhruba Kumar Bhattacharyya, and Jugal Kumar Kalita. "Gene Expression Data Generation." In Gene Expression Data Analysis. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429322655-3.

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Ruiz-Alonso, Maria, Jose Miravet-Valenciano, Pilar López, and Carlos Simón. "Endometrial Receptivity by Endometrial Receptivity Analysis (ERA) for Infertility." In Endometrial Gene Expression. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28584-5_6.

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Ferguson, Matthew L., and Daniel R. Larson. "Measuring Transcription Dynamics in Living Cells Using Fluctuation Analysis." In Imaging Gene Expression. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-526-2_4.

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Kim, Ju Han. "Gene Expression Data Analysis." In Genome Data Analysis. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_6.

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Barah, Pankaj, Dhruba Kumar Bhattacharyya, and Jugal Kumar Kalita. "Concluding Remarks and Research Challenges." In Gene Expression Data Analysis. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429322655-8.

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Barah, Pankaj, Dhruba Kumar Bhattacharyya, and Jugal Kumar Kalita. "Introduction." In Gene Expression Data Analysis. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429322655-1.

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Conference papers on the topic "Gene expression analysis"

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Boldura, Oana Mari, Jelena Savici, Calin Mircu, Cornel Balta, and Simona Marc. "VALIDATION OF REFERENCE GENES FOR RT-QPCR IN PORCINE OOCYTES CULTURED IN ANTIOXIDANT-ENRICHED MEDIA." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024v/4.2/s19.52.

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Reference genes are essential for normalising data in RT-qPCR experiments and are critical in reducing technical variability. This study aimed to identify and validate stable reference genes for accurate gene expression analysis in porcine oocytes matured in culture media supplemented with antioxidants. Five commonly used reference genes were assessed: PPIA, RPL4, GAPDH, YWHAZ, and TBP, [2, 3] in oocytes cultured under oxidative stress conditions induced by Vitamin C. The experiment involved four experimental groups, analyzed in triplicate using SYBR Green RT-qPCR. PPIA exhibited the lowest SE
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Lai, Yinglei. "The analysis of ordered changes of gene expression and gene-gene co-expression patterns." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729863.

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Guo, Ping, and Xiao-yan Deng. "Gene Expression Data Cluster Analysis." In 2009 WASE International Conference on Information Engineering (ICIE). IEEE, 2009. http://dx.doi.org/10.1109/icie.2009.153.

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Dhiraj, Kumar, Santanu Kumar Rath, and Abhishek Pandey. "Gene Expression Analysis Using Clustering." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162877.

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Ding, Chris H. Q. "Analysis of gene expression profiles." In the sixth annual international conference. ACM Press, 2002. http://dx.doi.org/10.1145/565196.565212.

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Yang, Andy C., Hui-Huang Hsu, and Ming-Da Lu. "Applying gene ontology to microarray gene expression data analysis." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551740.

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"Differential gene expression analysis in barley Nud gene mutants." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-350.

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Gonçalves, Paola Gyuliane. "Analysis of the potential prognostic of ELAVL2, FOCAD and MLLT3 in glioblastoma." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.363.

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Background: There is a crucial demand to identify molecular markers for cancer to improve the personalized treatment, diagnosis and prognosis. Our research group described a frequently deleted region (chr9p22.1-p21.3) in gliomas, with genes potentially important in the development of this tumor type, including ELAVL2, FOCAD and MLLT3. Objectives: Therefore, the aim of this study was to analyze the expression of those genes as potential biomarkers for glioblastoma (GBM) prognosis. Design and setting: The study was retrospective with samples collected at Barretos Cancer Hospital. Methods: Immuno
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Raut, Shital A., S. R. Sathe, and Adarsh Raut. "Bioinformatics: Trends in gene expression analysis." In 2010 International Conference on Bioinformatics and Biomedical Technology. IEEE, 2010. http://dx.doi.org/10.1109/icbbt.2010.5479003.

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Petre, Irina-Oana, and Catalin Buiu. "An integrated gene expression analysis approach." In 2015 E-Health and Bioengineering Conference (EHB). IEEE, 2015. http://dx.doi.org/10.1109/ehb.2015.7391442.

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Reports on the topic "Gene expression analysis"

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Gerald, William L. Gene Expression Analysis of Breast Cancer Progression. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada437751.

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Wang, Xuefel, Huining Kang, Chris Fields, et al. Application of multidisciplinary analysis to gene expression. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/918393.

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Bethel, E. Wes, Oliver Rubel, Gunther H. Weber, Bernd Hamann, and Hans Hagen. Visualization and Analysis of 3D Gene Expression Data. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/928239.

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Rosenberg, Jonathan. Gene Expression Analysis of Circulating Hormone Refractory Prostate Cancer. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada453368.

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Brufsky, Adam M. Determination of a Unique Pattern of Gene Expression in Node Positive Breast Cancer Using Serial Analysis of Gene Expression (SAGE). Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada417855.

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Brufsky, Adam M. Determination of a Unique Pattern of Gene Expression in Node Positive Breast Cancer Using Serial Analysis of Gene Expression (SAGE). Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada424196.

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Lers, Amnon, E. Lomaniec, S. Burd, A. Khalchitski, L. Canetti, and Pamela J. Green. Analysis of Senescence Inducible Ribonuclease in Tomato: Gene Regulation and Function. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7570563.bard.

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Natural leaf senescence has a negative influence on yield. Postharvest induced senescence contributes to the losses of quality in flowers, foliage, and vegetables. Strategies designed to control the senescence process in crop plants could therefore have great applied significance. Senescence is regulated by differential gene expression yet, functional characterization of the genes specifically induced and study of their expression control, is still in its infancy. Study of senescence-specific genes is required to allow identification of regulatory elements participating in senescence-induced e
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Peterson, Scott N. Anthrax: Gene Expression Analysis of the Early Stages of Infection. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada428329.

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Ahn, Jiyoung. Integrative Analysis of Genomewide Gene Expression for Prostate Cancer Prognosis. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada563213.

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Braam, Janet. Genetic analysis of the regulation of TCH gene expression, Final Report. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/939904.

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