Relevant bibliographies by topics / Microarray analysis

Academic literature on the topic 'Microarray analysis'

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Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Microarray analysis"

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Raczynski, Lech, Krzysztof Wozniak, Tymon Rubel, and Krzysztof Zaremba. "Application of Density Based Clustering to Microarray Data Analysis." International Journal of Electronics and Telecommunications 56, no. 3 (September 1, 2010): 281–86. http://dx.doi.org/10.2478/v10177-010-0037-9.

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Application of Density Based Clustering to Microarray Data AnalysisIn just a few years, gene expression microarrays have rapidly become a standard experimental tool in the biological and medical research. Microarray experiments are being increasingly carried out to address the wide range of problems, including the cluster analysis. The estimation of the number of clusters in datasets is one of the main problems of clustering microarrays. As a supplement to the existing methods we suggest the use of a density based clustering technique DBSCAN that automatically defines the number of clusters. The DBSCAN and other existing methods were compared using the microarray data from two datasets used for diagnosis of leukemia and lung cancer.
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García-Albert, L., F. Martín-Sánchez, A. García-Sáiz, and G. H. López-Campos. "Analysis and Management of HIV Peptide Microarray Experiments." Methods of Information in Medicine 45, no. 02 (2006): 158–62. http://dx.doi.org/10.1055/s-0038-1634060.

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Summary Objectives: To develop a tool for then easy and user-friendly management of peptide microarray experiments and for the use of the results of these experiments for the study the immune response against HIV virus infection in clinical samples. Methods: Applying bioinformatics and statistics for the analysis of data coming from microarray experiments as well as implementing a MIAME (Minimum Information About a Microarray Experiment) compliant database for managing and annotating experiments, results and samples. Results: We present a new tool for managing not only nucleic acid microarray experiments but also protein microarray experiments. From the analysis of experimental data, we can detect different profiles in the reactivity of the sera with different genotypes. Conclusions: We have developed a new tool for managing microarray data including clinical annotations for the samples as well as the capability of annotating other microarray formats different to those based on nucleic acids. The use of peptide microarrays and bioinformatics analysis opens a new scope for the characterization of the immune response, and analyzing and identifying the humoral response of viruses with different genotypes.
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Gibson, Greg. "Microarray Analysis." PLoS Biology 1, no. 1 (October 13, 2003): e15. http://dx.doi.org/10.1371/journal.pbio.0000015.

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Wenstrom, Katharine D. "Microarray Analysis." Obstetrics & Gynecology 124, no. 2, PART 1 (August 2014): 199–201. http://dx.doi.org/10.1097/aog.0000000000000407.

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Shangkuan, Wei-Chuan, Hung-Che Lin, Yu-Tien Chang, Chen-En Jian, Hueng-Chuen Fan, Kang-Hua Chen, Ya-Fang Liu, et al. "Risk analysis of colorectal cancer incidence by gene expression analysis." PeerJ 5 (February 15, 2017): e3003. http://dx.doi.org/10.7717/peerj.3003.

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Background Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. Objective Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples. Methods We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues. Results Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method. Conclusions Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.
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Cao, Yiwei, Sang-Jun Park, Akul Y. Mehta, Richard D. Cummings, and Wonpil Im. "GlyMDB: Glycan Microarray Database and analysis toolset." Bioinformatics 36, no. 8 (December 16, 2019): 2438–42. http://dx.doi.org/10.1093/bioinformatics/btz934.

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Abstract Motivation Glycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein–carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis. Results The current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures. Availability and implementation http://www.glycanstructure.org/glymdb. Supplementary information Supplementary data are available at Bioinformatics online.
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White, Christine A., and Lois A. Salamonsen. "A guide to issues in microarray analysis: application to endometrial biology." Reproduction 130, no. 1 (July 2005): 1–13. http://dx.doi.org/10.1530/rep.1.00685.

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Within the last decade, the development of DNA microarray technology has enabled the simultaneous measurement of thousands of gene transcripts in a biological sample. Conducting a microarray study is a multi-step process; starting with a well-defined biological question, moving through experimental design, target RNA preparation, microarray hybridisation, image acquisition and data analysis – finishing with a biological interpretation requiring further study. Advances continue to be made in microarray quality and methods of statistical analysis, improving the reliability and therefore appeal of microarray analysis for a wide range of biological questions. The purpose of this review is to provide both an introduction to microarray methodology, as well as a practical guide to the use of microarrays for gene expression analysis, using endometrial biology as an example of the applications of this technology. While recommendations are based on previous experience in our laboratory, this review also summarises the methods currently considered to be best practice in the field.
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Marjani, Sadie L., Daniel Le Bourhis, Xavier Vignon, Yvan Heyman, Robin E. Everts, Sandra L. Rodriguez-Zas, Harris A. Lewin, Jean-Paul Renard, Xiangzhong Yang, and X. Cindy Tian. "Embryonic gene expression profiling using microarray analysis." Reproduction, Fertility and Development 21, no. 1 (2009): 22. http://dx.doi.org/10.1071/rd08217.

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Microarray technology enables the interrogation of thousands of genes at one time and therefore a systems level of analysis. Recent advances in the amplification of RNA, genome sequencing and annotation, and the lower cost of developing microarrays or purchasing them commercially, have facilitated the analysis of single preimplantation embryos. The present review discusses the components of embryonic expression profiling and examines current research that has used microarrays to study the effects of in vitro production and nuclear transfer.
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Huang, Joe Xi, Dorothy Mehrens, Rick Wiese, Sandy Lee, Sun W. Tam, Steve Daniel, James Gilmore, Michael Shi, and Deval Lashkari. "High-Throughput Genomic and Proteomic Analysis Using Microarray Technology." Clinical Chemistry 47, no. 10 (October 1, 2001): 1912–16. http://dx.doi.org/10.1093/clinchem/47.10.1912.

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Abstract Background: High-density microarrays are ideally suited for analyzing thousands of genes against a small number of samples. The next step in the discovery process is to take the resulting genes of interest and rapidly screen them against thousands of patient samples, tissues, or cell lines to further investigate their involvement in disease risk or the response to medication. Methods: We used a microarray technology platform for both single-nucleotide polymorphisms (SNPs) and protein expression. Each microarray contains up to 250 elements that can be customized for each application. Slides contained either a 16- or 96-microarray format (4000–24 000 elements per slide), allowing the corresponding number of samples to be rapidly processed in parallel. Results: Results for SNP genotyping and protein profiling agreed with results of restriction fragment length polymorphism (RFLP) analysis or ELISA, respectively. Genotyping analyses, using the microarray technology, on large sample sets over multiple polymorphisms in the NAT2 gene were in full agreement with traditional methodologies, such as sequencing and RFLP analysis. The multiplexed protein microarray had correlation coefficients of 0.82–0.99 (depending on analyte) compared with ELISAs. Conclusions: The integrated microarray technology platform is adaptable and versatile, while offering the high-throughput capabilities needed for drug development and discovery applications.
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Wang, Zhiyou, Xiaoqing Huang, and Zhiqiang Cheng. "Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis." Biosensors 8, no. 3 (September 13, 2018): 85. http://dx.doi.org/10.3390/bios8030085.

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An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from SPRi video data. In demonstrations of the method, SPRi video data of different protein microarrays were processed by the method. Results show that our method can locate spots in the microarray accurately regardless of the microarray pattern, spot-background contrast, light nonuniformity and spotting defects, but also can provide address information of the spots.
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Dissertations / Theses on the topic "Microarray analysis"

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Wang, Tao. "Statistical design and analysis of microarray experiments." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains ix, 146 p.; also includes graphics (some col.) Includes bibliographical references (p. 145-146). Available online via OhioLINK's ETD Center
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Stephens, Nathan Wallace. "A Comparison of Microarray Analyses: A Mixed Models Approach Versus the Significance Analysis of Microarrays." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1115.

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DNA microarrays are a relatively new technology for assessing the expression levels of thousands of genes simultaneously. Researchers hope to find genes that are differentially expressed by hybridizing cDNA from known treatment sources with various genes spotted on the microarrays. The large number of tests involved in analyzing microarrays has raised new questions in multiple testing. Several approaches for identifying differentially expressed genes have been proposed. This paper considers two: (1) a mixed models approach, and (2) the Signiffcance Analysis of Microarrays.
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Lau, Kelvin Ee Ming. "Microarray analysis of Acidovorax temperans." Thesis, University of Auckland, 2008. http://hdl.handle.net/2292/5869.

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Bacteria belonging to the genus Acidovorax have been shown to be a consistent member of the activated sludge microbial community. Two phenotypic variants of A. temperans CB2 isolated locally from activated sludge exhibit noteworthy characteristics, such as the ability to form biofilms and flocs, which are critical microbial processes underlying all modern wastewater treatment systems. Gene expression microarray technology is a functional genomics platform that enables the simultaneous interrogation of all expressed transcripts during normal cell ontogeny, or in response to specific environmental stimuli. Microarray technology offers the opportunity to investigate gene expression changes relevant to key processes in wastewater treatment, using A. temperans as a model organism. The aims of this research were to develop a full genome microarray platform for A. temperans CB2 and to use this microarray platform to investigate major differences in gene expression between the Hpos and Hneg phenotypic variants. An optimised gene expression microarray platform was established through the assessment of various experimental methods, such as RNA extraction, RNA amplification, microarray probe design, and quantitative PCR. Using the microarray platform, gene expression comparisons were obtained for planktonic broth cultures, static biofilms and bacterial colonies. Gene expression analyses have provided insights into the complex developmental processes involved in the transition from planktonic cells to stages of initial attachment, cell proliferation, biofilm maturation and nutrient limitation during the formation of A. temperans biofilms. Factors that have been identified in other bacterial systems such as type IV pili and activation of stress responses were also observed in A. temperans biofilms. In addition, several intriguing classes of genes, such as transcriptional regulators, a toxinantitoxin gene cassette, and nitrate metabolism were also found to be differentially expressed during the formation of A. temperans biofilm. The incorporation of microarray technology with other functional genomics techniques to investigate molecular mechanisms underlying the complex processes occurring in wastewater treatment will provide a scientific basis to improve the reliability of current wastewater treatment strategies and for the development of new treatment technologies.
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O'Neill, Paul. "Improved analysis of microarray images." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435755.

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Selvaraja, Sudarshan. "Microarray Data Analysis Tool (MAT)." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806.

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Stephens, Nathan W. "A comparison of genetic microarray analyses : a mixed models approach versus the significance analysis of microarrays /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1604.pdf.

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Dvergsten, Erik C. "A Weighted Gene Co-expression Network Analysis for Streptococcus sanguinis Microarray Experiments." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4430.

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Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes. Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene modules were determined from the connection strengths, and various network connectivity measures were calculated. S. sanguinis gene expression profile data was loaded for 2272 genes and 14 samples with 3 replicates each. The soft thresholding power β=6 was chosen to maximize R2 while maintaining a high mean number of connections. Nine modules were found. Possible meta-modules were found to be: Module 1: Blue & Green, Module 2: Pink, Module 3: Yellow, Brown & Red, Module 4: Black, Module 5: Magenta & Turquoise. The absolute value of module membership was found to be highly positively correlated with intramodular connectivity. Each of the nine modules were examined. Two methods (intramodular connectivity and TOM-based connectivity followed by network mapping) for identifying candidate hub genes were performed. Most modules provided similar results between the two methods. Similar rankings between the two methods can be considered equivalent and both can be used to detect candidate hub genes. Gene ontology information was unavailable to help select a module of interest. This network analysis would help researchers create new research hypotheses and design experiments for validation of candidate hub genes in biologically important modules.
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Fellenberg, Kurt. "Storage and analysis of microarray data." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964718839.

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Hare, Brian K. Dinakarpandian Deendayal. "Feature selection in DNA microarray analysis." Diss., UMK access, 2004.

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Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2004.
"A thesis in computer science." Typescript. Advisor: D. Dinakarpandian. Vita. Title from "catalog record" of the print edition Description based on contents viewed Feb. 24, 2006. Includes bibliographical references (leaves 81-86 ). Online version of the print edition.
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Hultin, Emilie. "Genetic Sequence Analysis by Microarray Technology." Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4330.

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Books on the topic "Microarray analysis"

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Michael, Korenberg J. Microarray Data Analysis. New Jersey: Humana Press, 2007. http://dx.doi.org/10.1385/1597453900.

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Agapito, Giuseppe, ed. Microarray Data Analysis. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1839-4.

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Korenberg, Michael J., ed. Microarray Data Analysis. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5.

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Guzzi, Pietro Hiram, ed. Microarray Data Analysis. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3173-6.

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Tuimala, Jarno, and M. Minna Laine. DNA microarray data analysis. [Espoo]: CSC - Scientific Computing, 2003.

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Shinmura, Shuichi. High-dimensional Microarray Data Analysis. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5998-9.

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Wu, Catherine J., ed. Protein Microarray for Disease Analysis. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-043-0.

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Shoemaker, Jennifer S., and Simon M. Lin, eds. Methods of Microarray Data Analysis. Boston: Kluwer Academic Publishers, 2005. http://dx.doi.org/10.1007/b100565.

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Lin, Simon M., and Kimberly F. Johnson, eds. Methods of Microarray Data Analysis. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0873-1.

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Phillip, Stafford, ed. Methods in microarray normalization. Boca Raton: CRC Press, 2008.

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Book chapters on the topic "Microarray analysis"

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Page, Grier P., Stanislav O. Zakharkin, Kyoungmi Kim, Tapan Mehta, Lang Chen, and Kui Zhang. "Microarray Analysis." In Topics in Biostatistics, 409–30. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_20.

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Patel, Nisha R., Michael L. Wong, Anthony E. Dragun, Stephan Mose, Bernadine R. Donahue, Jay S. Cooper, Filip T. Troicki, et al. "Microarray Analysis." In Encyclopedia of Radiation Oncology, 501. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-85516-3_257.

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Weeraratna, Ashani T., and Dennis D. Taub. "Microarray Data Analysis." In Microarray Data Analysis, 1–16. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_1.

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Suárez-Fariñas, Mayte, and Marcelo O. Magnasco. "Comparing Microarray Studies." In Microarray Data Analysis, 139–52. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_8.

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Samanta, Manoj Pratim, Waraporn Tongprasit, and Viktor Stolc. "In-Depth Query of Large Genomes Using Tiling Arrays." In Microarray Data Analysis, 163–73. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_10.

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Bilke, Sven, and Javed Khan. "Analysis of Comparative Genomic Hybridization Data on cDNA Microarrays." In Microarray Data Analysis, 175–86. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_11.

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Juric, Dejan, Claudia Bredel, Branimir I. Sikic, and Markus Bredel. "Integrated High-Resolution Genome-Wide Analysis of Gene Dosage and Gene Expression in Human Brain Tumors." In Microarray Data Analysis, 187–202. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_12.

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MacDonald, Tobey J., Ian F. Pollack, Hideho Okada, Soumyaroop Bhattacharya, and James Lyons-Weiler. "Progression-Associated Genes in Astrocytoma Identified by Novel Microarray Gene Expression Data Reanalysis." In Microarray Data Analysis, 203–21. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_13.

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Osborne, John D., Lihua (Julie) Zhu, Simon M. Lin, and Warren A. Kibbe. "Interpreting Microarray Results With Gene Ontology and MeSH." In Microarray Data Analysis, 223–41. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_14.

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Ochs, Michael F., Aidan J. Peterson, Andrew Kossenkov, and Ghislain Bidaut. "Incorporation of Gene Ontology Annotations to Enhance Microarray Data Analysis." In Microarray Data Analysis, 243–54. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_15.

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Conference papers on the topic "Microarray analysis"

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LIATSIS, P., and M. A. NAZARBOLAND. "MICROARRAY IMAGE ANALYSIS." In Proceedings of the 9th International Workshop on Systems, Signals and Image Processing. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776266_0078.

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Yang, Youngik, Jong Youl Choi, Kwangmin Choi, Marlon Pierce, Dennis Gannon, and Sun Kim. "BioVLAB-Microarray: Microarray Data Analysis in Virtual Environment." In 2008 IEEE Fourth International Conference on eScience (eScience). IEEE, 2008. http://dx.doi.org/10.1109/escience.2008.57.

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Tozduman, Ersin, and Songul Albayrak. "cDNA microarray image analysis." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130308.

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Han-Yu Chuang, Hongfang Liu, Fang-An Chen, Cheng-Yan Kao, and D. F. Hsu. "Combination methods in microarray analysis." In 7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/ispan.2004.1300548.

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Guse, Frank, and Jakob Bleicher. "Sophisticated lenses for microarray analysis." In BiOS 2001 The International Symposium on Biomedical Optics, edited by Michael L. Bittner, Yidong Chen, Andreas N. Dorsel, and Edward R. Dougherty. SPIE, 2001. http://dx.doi.org/10.1117/12.427975.

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Qi, Fei, and Chengying Hua. "Efficient automated microarray image analysis." In Second International Conference on Image and Graphics, edited by Wei Sui. SPIE, 2002. http://dx.doi.org/10.1117/12.477198.

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Muresan, L., B. Heise, E. P. Klement, and J. Kybic. "Quantitative analysis of microarray images." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530295.

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Mecklenburg, Michael, and Bin Xie. "Microarray platform for omics analysis." In International Conference on Sensing units and Sensor Technology, edited by Yikai Zhou and Shunqing Xu. SPIE, 2001. http://dx.doi.org/10.1117/12.440149.

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Osareh, Alireza, and Bita Shadgar. "Microarray data analysis for cancer classification." In 2010 5th International Symposium on Health Informatics and Bioinformatics. IEEE, 2010. http://dx.doi.org/10.1109/hibit.2010.5478893.

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Zervakis, M., M. E. Blazadonakis, A. Banti, D. Kafetzopoulos, V. Danilatou, and M. Tsiknakis. "Performance validation of microarray analysis methods." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696688.

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Reports on the topic "Microarray analysis"

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Splitter, Gary, and Menachem Banai. Microarray Analysis of Brucella melitensis Pathogenesis. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7709884.bard.

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Original Objectives 1. To determine the Brucella genes that lead to chronic macrophage infection. 2. To identify Brucella genes that contribute to infection. 3. To confirm the importance of Brucella genes in macrophages and placental cells by mutational analysis. Background Brucella spp. is a Gram-negative facultative intracellular bacterium that infects ruminants causing abortion or birth of severely debilitated animals. Brucellosis continues in Israel, caused by B. melitensis despite an intensive eradication campaign. Problems with the Rev1 vaccine emphasize the need for a greater understanding of Brucella pathogenesis that could improve vaccine designs. Virulent Brucella has developed a successful strategy for survival in its host and transmission to other hosts. To invade the host, virulent Brucella establishes an intracellular niche within macrophages avoiding macrophage killing, ensuring its long-term survival. Then, to exit the host, Brucella uses placenta where it replicates to high numbers resulting in abortion. Also, Brucella traffics to the mammary gland where it is secreted in milk. Missing from our understanding of brucellosis is the surprisingly lillie basic information detailing the mechanisms that permit bacterial persistence in infected macrophages (chronic infection) and dissemination to other animals from infected placental cells and milk (acute infection). Microarray analysis is a powerful approach to determine global gene expression in bacteria. The close genomic similarities of Brucella species and our recent comparative genomic studies of Brucella species using our B. melitensis microarray, suqqests that the data obtained from studying B. melitensis 16M would enable understanding the pathogenicity of other Brucella organisms, particularly the diverse B. melitensis variants that confound Brucella eradication in Israel. Conclusions Results from our BARD studies have identified previously unknown mechanisms of Brucella melitensis pathogenesis- i.e., response to blue light, quorum sensing, second messenger signaling by cyclic di-GMP, the importance of genomic island 2 for lipopolysaccharide in the outer bacterial membrane, and the role of a TIR domain containing protein that mimics a host intracellular signaling molecule. Each one of these pathogenic mechanisms offers major steps in our understanding of Brucella pathogenesis. Strikingly, our molecular results have correlated well to the pathognomonic profile of the disease. We have shown that infected cattle do not elicit antibodies to the organisms at the onset of infection, in correlation to the stealth pathogenesis shown by a molecular approach. Moreover, our field studies have shown that Brucella exploit this time frame to transmit in nature by synchronizing their life cycle to the gestation cycle of their host succumbing to abortion in the last trimester of pregnancy that spreads massive numbers of organisms in the environment. Knowing the bacterial mechanisms that contribute to the virulence of Brucella in its host has initiated the agricultural opportunities for developing new vaccines and diagnostic assays as well as improving control and eradication campaigns based on herd management and linking diagnosis to the pregnancy status of the animals. Scientific and Agricultural Implications Our BARD funded studies have revealed important Brucella virulence mechanisms of pathogenesis. Our publication in Science has identified a highly novel concept where Brucella utilizes blue light to increase its virulence similar to some plant bacterial pathogens. Further, our studies have revealed bacterial second messengers that regulate virulence, quorum sensing mechanisms permitting bacteria to evaluate their environment, and a genomic island that controls synthesis of its lipopolysaccharide surface. Discussions are ongoing with a vaccine company for application of this genomic island knowledge in a Brucella vaccine by the U.S. lab. Also, our new technology of bioengineering bioluminescent Brucella has resulted in a spin-off application for diagnosis of Brucella infected animals by the Israeli lab by prioritizing bacterial diagnosis over serological diagnosis.
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Haghighi, F. Kernel Principle Component Analysis of Microarray Data. Final Report. Office of Scientific and Technical Information (OSTI), November 2003. http://dx.doi.org/10.2172/823317.

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Khatri, Purvesh, Dechang Chen, Jaques Reifman, Craig M. Lilly, and Larry A. Sonna. Software Tool for Analysis of Variance of DNA Microarray Data. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada460048.

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Wang, Jian, Joy D. Van Nostrand, Zhili He, Liyou Wu, Ye Deng, Xu Zhang, Jizhong Zhou, and Guanghe Li. Microarray-based analysis of survival of soil microbial community during ozonation. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/986918.

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Gopalan, Banu, Christian Posse, Antonio P. Sanfilippo, Mary Stenzel-Poore, S. L. Stevens, Jose Castano, Nathaniel Beagley, et al. Aligning ontologies and integrating textual evidence for pathway analysis of microarray data. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/948766.

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Marchionni, Enrica, Daniele Guadagnolo, Gioia Mastromoro, and Antonio Pizzuti. Diagnostic yield of prenatal Exome Sequencing in fetal Central Nervous System Anomalies: systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2023. http://dx.doi.org/10.37766/inplasy2023.5.0003.

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Review question / Objective: The aim of this study is to assess the incremental diagnostic yield of prenatal exome sequencing analysis after inconclusive result of karyotype and Chromosomal Microarray Analysis in Central Nervous System fetal anomalies detected by ultrasound. Eligibility criteria: Inclusion criteria: papers describing fetuses with the indication to perform genome-wide sequencing studies based on prenatal imaging findings who underwent previous inconclusive karyotype and Chromosomal Microarray Analyses. The diagnostic yields of prenatal exome sequencing analysis OR prenatal genome sequencing analysis (with ≥20–30x depth of coverage and including only Single Nucleotide Variants) will be pooled in a meta-analysis. Exclusion Criteria: case reports and papers describing less than 5 cases; papers not describing the application of genome-wide sequencing studies based on prenatal imaging findings; papers describing genome-wide sequencing studies performed after negative targeted panels; papers describing fetuses with recurrent phenotypes as an explicitly selection criterium.
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Tooker, B. C., and Timothy S. Stahly. Microarray Analysis of Gene Expression Essential to Energetic Efficiency in a Porcine Model of Obesity. Ames (Iowa): Iowa State University, January 2005. http://dx.doi.org/10.31274/ans_air-180814-1077.

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Katzir, Nurit, James Giovannoni, Marla Binzel, Efraim Lewinsohn, Joseph Burger, and Arthur Schaffer. Genomic Approach to the Improvement of Fruit Quality in Melon (Cucumis melo) and Related Cucurbit Crops II: Functional Genomics. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7592123.bard.

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Background: Genomics tools for enhancement of melon research, with an emphasis on fruit, were developed through a previous BARD project of the PIs (IS -333-02). These included the first public melon EST collection, a database to relay this information to the research community and a publicly available microarray. The current project (IS-3877- 06) aimed to apply these tools for identification of important genes for improvement of melon (Cucumis melo) fruit quality. Specifically, the research plans included expression analysis using the microarray and functional analyses of selected genes. The original project objectives, as they appeared in the approved project, were: Objective 1: Utilization of a public melon microarray developed under the existing project to characterize melon transcriptome activity during the ripening of normal melon fruit (cv. Galia) in order to provide a basis for both a general view of melon transcriptome activity during ripening and for comparison with existing transcriptome data of developing tomato and pepper fruit. Objective 2: Utilization of the same public melon microarray to characterize melon transcriptome activity in lines available in the collection of the Israeli group, focusing on sugar, organic acids and aroma metabolism, so as to identify potentially useful candidates for functional analysis and possible manipulation, through comparison with the general fruit development profile resulting from (1) above. Objective 3: Expansion of our existing melon EST database to include publicly available gene expression data and query tools, as the US group has done with tomato. Objective 4: Selection of 6-8 candidate genes for functional analysis and development of DNA constructs for repression or over-expression. Objective 5: Creation of transgenic melon lines, or transgenic heterologous systems (e.g. E. coli or tomato), to assess putative functions and potential as tools for molecular enhancement of melon fruit quality, using the candidate gene constructs from (4).
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Katzir, Nurit, James Giovannoni, and Joseph Burger. Genomic approach to the improvement of fruit quality in melon (Cucumis melo) and related cucurbit crops. United States Department of Agriculture, June 2006. http://dx.doi.org/10.32747/2006.7587224.bard.

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Fruit quality is determined by numerous genetic traits that affect taste, aroma, texture, pigmentation, nutritional value and duration of shelf-life. The molecular basis of many of these important traits is poorly understood and it’s understanding offers an excellent opportunity for adding value to agricultural products. Improvement of melon fruit quality was the primary goal of the project. The original objectives of the project were: The isolation of a minimum of 1000 fruit specific ESTs. The development of a microarray of melon fruit ESTs. The analysis of gene expression in melon using melon and tomato fruit enriched microarrays. A comprehensive study of fruit gene expression of the major cucurbit crops. In our current project we have focused on the development of genomics tools for the enhancement of melon research with an emphasis on fruit, specifically the first public melon EST collection. We have also developed a database to relay this information to the research community and developed a publicly available microarray. The release of this information was one of the catalysts for the establishment of the International Cucurbit Genomic Initiative (ICuGI, Barcelona, Spain, July 2005) aimed at collecting and generating up to 100,000 melon EST sequences in 2006, leveraging a significant expansion of melon genomic resources. A total of 1000 ESTs were promised under the original proposal (Objective 1). Non-subtracted mature fruit and young fruit flesh of a climacteric variety in addition to a non-climacteric variety resulted in the majority of additional EST sequences for a total of 4800 attempted reads. 3731 high quality sequences from independent ESTs were assembled, representing 2,467 melon unigenes (1,873 singletons, 594 contigs). In comparison, as of June 2004, a total of 170 melon mRNA sequences had been deposited in GENBANK. The current project has thus resulted in nearly five- fold the number of ESTs promised and ca. 15-fold increase in the depth of publicly available melon gene sequences. All of these sequences have been deposited in GENBANK and are also available and searchable via multiple approaches in the public database (http://melon.bti.cornell.edu). Our database was selected as the central location for presentation of public melon EST data of the International Cucurbit Genomic Initiative. With the available unigenes we recently constructed a microarray, which was successfully applied in hybridizations (planned public release by August 2006). Current gene expression analyses focus on fruit development and on comparative studies between climacteric and non-climacteric melons. Earlier, expression profiling was conducted using macroarrays developed at the preliminary stage of the project. This analysis replaced the study of tomato microarray following the recommendations of the reviewers and the panel of the original project. Comparative study between melon and other cucurbit crops have begun, mainly with watermelon, in collaboration with Dr. Amnon Levi (USDA-ARS). In conclusion, all four objectives have been addressed and achieved. In the continuation project that have been approved we plan to apply the genomic tools developed here to achieve detailed functional analyses of genes associated with major metabolic pathway.
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Szallasi, Zoltan. CDNA Microarray Based Comparative Gene Expression Analysis of Primary Breast Tumors Versus In Vitro Transformed Neoplastic Breast Epithelium. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada401181.

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