Relevant bibliographies by topics / Gene expression analysis

Academic literature on the topic 'Gene expression analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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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 (May 25, 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 (November 20, 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 were the optimal reference genes, both EF1α and 28S were suitable for experiments of different tissues, whereas for herbicide treatments, GAPDH and ACT were suitable for normalizations of expression data. In addition, GAPDH and EF1α were the suitable reference genes.
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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 (April 30, 2016): 16–23. http://dx.doi.org/10.9756/bijdm.8135.

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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 (February 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 (February 15, 2012): 181–90. http://dx.doi.org/10.1002/bmb.20588.

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Mikami, Koji. "Requirement for Different Normalization Genes for Quantitative Gene Expression Analysis Under Abiotic Stress Conditions in ‘Bangia’ sp. ESS1." Journal of Aquatic Research and Marine Sciences 02, no. 03 (August 28, 2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.202037.

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Mikami, Koji. "Requirement for Different Normalization Genes for Quantitative Gene Expression Analysis Under Abiotic Stress Conditions in ‘Bangia’ sp. ESS1." Journal of Aquatic Research and Marine Sciences 02, no. 03 (August 28, 2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.203037.

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Winter, Holger, Kerstin Korn, and Rudolf Rigler. "Direct Gene Expression Analysis." Current Pharmaceutical Biotechnology 5, no. 2 (April 1, 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 (April 2011): 1–31. http://dx.doi.org/10.1089/gen.31.7.13.

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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 how perturbations alter each cluster (integrated response co-efficients) and how strongly those clusters affect an output response is calculated (elasticity co-efficients). The product of these values quantifies how an input changes a response through each cluster (partial response co-efficients). Once identified, important clusters that mediate a large proportion of the response may suggest targets for investigation of, for example, disease mechanisms, and way of modifying that response, such as potential knockout, overexpression or drug targets. Two published datasets were used throughout the development of the method. This determined the requirements of a suitable dataset, and involved the creation of a test to exclude problematic experiments from the dataset. Analyses of the two datasets using the final method reveal that two mRNA clusters transmit most of the response of yeast doubling time to galactose; one contains mainly galactose metabolic genes, and the other a regulatory gene. Analysis of the response of yeast relative fitness to 2-deocy-D-glucose reveals that control is distributed between several mRNA clusters. Monte Carlo analysis revealed that the co-efficients were not statistically significant, due to the large amount of experimental error in the dataset. However, modular regulation analysis should become more applicable in practice as microarray technology is improving rapidly.
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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 pathways that are relevant in breast cancer using pathway and network analyses of new and existing in vivo and in vitro microarray gene expression data. The oestrogen signalling pathway was the main focus of this thesis due to its documented importance in the pathogenesis of breast cancer. Analyses of gene expression differences in New Zealand breast tumours according to oestrogen receptor (ER) status revealed differentially regulated genes such as ESR1, GATA3 and EGFR, which have also been reported in other breast cancer microarray studies. The analyses of a collaboratively assembled 960-tumour dataset of clinical breast cancer microarray data revealed differentially regulated pathways involving BCL2, ESR1, EGFR, MYC and NFKB between ER positive and negative tumours. We also identified a principal component of oestrogen activity using the gene expression data from our collated 960-tumour dataset, that could be used alongside ESR1 mRNA and ER protein expression (from immunohistochemistry) to stratify breast cancer patients more accurately. The generation of an in vitro siRNA perturbation dataset using MCF7 breast cancer cells, and its analyses using gene networks has identified relationships between genes that appear to operate both in vitro and in vivo. There were more highly correlated gene pairs shared between the MCF7 dataset and luminal A tumours than between this dataset and other tumour subtypes. The identification of key molecular pathways and master regulators operating in breast tumours from gene expression data may improve our understanding of the biology behind breast cancer. This knowledge can be used in the future to help integrate gene expression data with clinicohistopathological data to improve diagnostic and therapeutic decision-making for patients.
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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 proposed another meta-analytic approach that incorporates informative weights in the random effects meta-analysis models. We compared the proposed methods with the standard and existing meta-analytic techniques in the classical and Bayesian frameworks. We demonstrate our results through a series of simulations and application in gene expression neurodegenerative diseases.
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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 work involved high dimensional data. Lastly the Bayesian model averaging approach was developed to incorporate model uncertainty in the prediction of survival.
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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 telomere length in CML. Additionally, expression of c-Myc, which increases hTERT transcription, correlated with hTERT expression suggesting decreased hTERT is partly caused by reduced c-Myc. hTERT promoter methylation and mutation status were investigated and this revealed that the hTERT promoter was not methylated and mutation rates were low suggesting that these are not contributing to reduced hTERT expression. cDNA microarrays were used to analyse gene expression in neutrophils of patients with Essential Thrombocythaemia (ET) harbouring the JAK2 V617F mutation which, like the BCRlABL translocation in CML, results in an activated kinase. Neutrophils of ET patients exhibited a gene expression profile close to that of controls despite the presence of the mutation. Affymetrix microarrays were used to investigate the role of telomerase related genes in Myelodysplastic Syndromes (MDS). Genes decreased in patients with del(5q) include positive regulators of telomere length and genes with higher expression were associated with increased telomerase activity.
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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. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7834-2.

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

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Nielsen, Kåre Lehmann, ed. Serial Analysis of Gene Expression (SAGE). Totowa, NJ: 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. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/b97411.

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

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

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Gregory, Bock, Marsh Joan, and Ciba Foundation, eds. Genetic analysis of tumour suppression. Chichester: Wiley, 1989.

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

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

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Pullen, Céline. Cloning and expression analysis of the RBM3 gene. Sudbury, Ont: Laurentian University, 2003.

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Book chapters on the topic "Gene expression analysis"

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Gondro, Cedric. "Gene Expression Analysis." In Use R!, 163–200. Cham: 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, 153–67. Cham: 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, 219–60. Boca Raton: 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, 145–218. Boca Raton: 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, 39–52. Boca Raton: 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, 91–102. Cham: 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, 47–60. Totowa, NJ: 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, 95–120. Singapore: 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, 295–300. Boca Raton: 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, 1–26. Boca Raton: 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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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. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/565196.565212.

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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: Immunohistochemistry reactions were performed for ELAVL2, FOCAD and MLLT3 proteins in 83 GBM samples. The reactions were evaluated using scores of intensity and extension, ranging from 0 to 6 in total. Patients with expression scores between 0 and 2 were considered negative for the expression of the specific gene, and those with scores between 3 and 6 were considered positive. Clinicopathological and molecular data from patients (age, gender, tumor location, KPS, and overall survival) were correlated with the expression of each gene. Results: ELAVL2-expressing tumors showed a trend to develop in the temporal lobe (P=0.052), whereas they were not found in the frontal lobe. Patients with FOCAD expression were older (>45y.o., p<0.001). Overall survival was not influenced by ELAVL2 or FOCAD expression. Patients with MLLT3 expression presented a marginal improved overall survival (p=0.077) when compared with patients without expression. Conclusion: Although there was a correlation of ELAVL2 and FOCAD with tumor location and age, respectively, these genes did not show prognostic potential in glioblastomas. MLLT3 showed potential prognostic, albeit more studies are warranted with larger cohorts.
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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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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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Odibat, Omar, Chandan K. Reddy, and Craig N. Giroux. "Differential biclustering for gene expression analysis." In the First ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854815.

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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. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada437751.

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Wang, Xuefel, Huining Kang, Chris Fields, Jim R. Cowie, George S. Davidson, David Michael Haaland, Valeriy Sibirtsev, et al. Application of multidisciplinary analysis to gene expression. Office of Scientific and Technical Information (OSTI), January 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), October 2007. http://dx.doi.org/10.2172/928239.

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Rosenberg, Jonathan. Gene Expression Analysis of Circulating Hormone Refractory Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, January 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). Fort Belvoir, VA: Defense Technical Information Center, June 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). Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada424196.

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Peterson, Scott N. Anthrax: Gene Expression Analysis of the Early Stages of Infection. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada428329.

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Ahn, Jiyoung. Integrative Analysis of Genomewide Gene Expression for Prostate Cancer Prognosis. Fort Belvoir, VA: Defense Technical Information Center, May 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), October 2008. http://dx.doi.org/10.2172/939904.

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Ahn, Jiyoung. Integrative Analysis of Genome-wide Gene Expression for Prostate Cancer Prognosis. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada552228.

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