Relevant bibliographies by topics / CDNA microarray

Academic literature on the topic 'CDNA microarray'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "CDNA microarray"

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Handley, Daniel, Nicoleta Serban, David G. Peters, and Clark Glymour. "Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 6, 2004): 1–2. http://dx.doi.org/10.2202/1544-6115.1091.

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We discuss our concerns regarding the reliability of data generated by spotted cDNA microarrays. Two types of error we highlight are cross-hybridization artifact due to sequence homologies and sequence errors in the cDNA used for spotting on microarrays. We feel that statisticians who analyze microarray data should be aware of these sources of unreliability intrinsic to cDNA microarray design and use.
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Wang, Zidong, Bachar Zineddin, Jinling Liang, Nianyin Zeng, Yurong Li, Min Du, Jie Cao, and Xiaohui Liu. "cDNA microarray adaptive segmentation." Neurocomputing 142 (October 2014): 408–18. http://dx.doi.org/10.1016/j.neucom.2014.03.052.

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Pérez-Enciso, Miguel, Miguel A. Toro, Michel Tenenhaus, and Daniel Gianola. "Combining Gene Expression and Molecular Marker Information for Mapping Complex Trait Genes: A Simulation Study." Genetics 164, no. 4 (August 1, 2003): 1597–606. http://dx.doi.org/10.1093/genetics/164.4.1597.

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Abstract A method for mapping complex trait genes using cDNA microarray and molecular marker data jointly is presented and illustrated via simulation. We introduce a novel approach for simulating phenotypes and genotypes conditionally on real, publicly available, microarray data. The model assumes an underlying continuous latent variable (liability) related to some measured cDNA expression levels. Partial least-squares logistic regression is used to estimate the liability under several scenarios where the level of gene interaction, the gene effect, and the number of cDNA levels affecting liability are varied. The results suggest that: (1) the usefulness of microarray data for gene mapping increases when both the number of cDNA levels in the underlying liability and the QTL effect decrease and when genes are coexpressed; (2) the correlation between estimated and true liability is large, at least under our simulation settings; (3) it is unlikely that cDNA clones identified as significant with partial least squares (or with some other technique) are the true responsible cDNAs, especially as the number of clones in the liability increases; (4) the number of putatively significant cDNA levels increases critically if cDNAs are coexpressed in a cluster (however, the proportion of true causal cDNAs within the significant ones is similar to that in a no-coexpression scenario); and (5) data reduction is needed to smooth out the variability encountered in expression levels when these are analyzed individually.
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Yang, Y. H. "Analysis of cDNA microarray images." Briefings in Bioinformatics 2, no. 4 (January 1, 2001): 341–49. http://dx.doi.org/10.1093/bib/2.4.341.

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Emi, Mitsuru. "cDNA Microarray and SNP Analysis." Journal of Nippon Medical School 68, no. 5 (2001): 411–12. http://dx.doi.org/10.1272/jnms.68.411.

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Smyth, Gordon K., and Terry Speed. "Normalization of cDNA microarray data." Methods 31, no. 4 (December 2003): 265–73. http://dx.doi.org/10.1016/s1046-2023(03)00155-5.

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Mizukami, Satomi, Yoshiteru Suzuki, Emiko Kitagawa, and Hitoshi Iwahashi. "Standardization of cDNA microarray technology for toxicogenomics; essential data for initiating cDNA microarray studies." Chem-Bio Informatics Journal 4, no. 2 (2004): 38–55. http://dx.doi.org/10.1273/cbij.4.38.

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Liang, Mingyu, Amy G. Briggs, Elizabeth Rute, Andrew S. Greene, and Allen W. Cowley. "Quantitative assessment of the importance of dye switching and biological replication in cDNA microarray studies." Physiological Genomics 14, no. 3 (August 15, 2003): 199–207. http://dx.doi.org/10.1152/physiolgenomics.00143.2002.

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Dye switching and biological replication substantially increase the cost and the complexity of cDNA microarray studies. The objective of the present analysis was to quantitatively assess the importance of these procedures to provide a quantitative basis for decision-making in the design of microarray experiments. Taking advantage of the unique characteristics of a published data set, the impact of these procedures on the reliability of microarray results was calculated. Adding a second microarray with dye switching substantially increased the correlation coefficient between observed and predicted ln(ratio) values from 0.38 ± 0.06 to 0.62 ± 0.04 ( n = 12) and the outlier concordance from 21 ± 3% to 43 ± 4%. It also increased the correlation with the entire set of microarrays from 0.60 ± 0.04 to 0.79 ± 0.04 and the outlier concordance from 31 ± 6% to 58 ± 5% and tended to improve the correlation with Northern blot results. Adding a second microarray to include biological replication also improved the performance of these indices but often to a lesser degree. Inclusion of both procedures in the second microarray substantially improved the consistency with the entire set of microarrays but had minimal effect on the consistency with predicted results. Analysis of another data set generated using a different cDNA labeling method also supported a significant impact of dye switching. In conclusion, both dye switching and biological replication substantially increased the reliability of microarray results, with dye switching likely having even greater benefits. Recommendations regarding the use of these procedures were proposed.
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Kuzhali, S. Elavaar, and Suresh D. S. "Collaborative Priors with SVD for Denoising of cDNA Microarray Images." Indian Journal of Science and Technology 12, no. 37 (October 10, 2019): 1–15. http://dx.doi.org/10.17485/ijst/2019/v12i37/147036.

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Becker, Kevin G. "The sharing of cDNA microarray data." Nature Reviews Neuroscience 2, no. 6 (June 2001): 438–40. http://dx.doi.org/10.1038/35077580.

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Dissertations / Theses on the topic "CDNA microarray"

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Fraser, Karl. "cDNA microarray image analysis : a fully automated framework." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429240.

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Stanzel, Sven. "Optimale statistische Versuchsplanung dreifaktorieller Zwei-Farben-cDNA-Microarray-Experimente /." Dortmund, 2008. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000254108.

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Soneji, Sharnit. "Statistical Analysis of cDNA Microarray Directed by Gene Function." Thesis, Birkbeck (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487759.

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Microarrays allow the expression level of thousands of genes to be measured simultaneously. This study will address analytical issues predominantly concerned with eDNA arrays. These include normalisation and data preprocessing, leading to an assessment of cluster analysis and the integration of database information to elucidate functional classes of biological relevance. This is then extended further to classify genes of unknown function using Markov Random Fields. I Modelling of uneven surface trends were considered in a new 2D-normalisation method which outperformed the popular loess method which concentrated on printing pin effects. With respect to cluster analysis, a new method to identify the number of clusters in higher dimension da~a is proposed which provides a visual way of determining at which point over-fitting of the data will occur. Once partitioned, functional information was incorporated to find enrichment of classes in clusters using a new application of X2 bootstrapping, which provides a very robust way of identifying these groups. A novel use of correspondence analysis was applied to the contingency tables produced from the cluster over class analysis which was used to show that functionally related groups were acting in concert when scrutinising the projection' of these classes onto three dimensions. The last part of this study attempted the use of Markov Random Fields to assign function to genes of unknown function using M. tuberculosis and E. coli data. The ability to determine function from the data used in this work was limited, but the method implemented in this study showed improvement over previous attempts.
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Jouenne, Vincent Y. "Critical Issues in the Processing of cDNA Microarray Images." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/33960.

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Microarray technology enables simultaneous gene expression level monitoring for thousands of genes. While this technology has now been recognized as a powerful and cost-effective tool for large-scale analysis, the many systematic sources of experimental variations introduce inherent errors in the extracted data. Data is gathered by processing scanned images of microarray slides. Therefore robust image processing is particularly important and has a large impact on downstream analysis. The processing of the scanned images can be subdivided in three phases: gridding, segmentation and data extraction. To measure the gene expression levels, the processing of cDNA microarray images must overcome a large set of issues in these three phases that motivates this study. This study presents automatic gridding methods and compares their performances. Two segmentation techniques already used, the Seeded Region Growing Algorithm and the Mann-Whitney Test, are examined. We present limitations of these techniques. Finally, we studied the data extraction method used in MicroArray Suite (MS), a microarray analysis software, via synthetic images and explain its intricacies.
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Wu, Meng. "Data mining cDNA microarray experiment with a GEE approach /." Electronic version (PDF), 2004. http://dl.uncw.edu/etd/2004/wum/mengwu.pdf.

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Hintze, Eric Poole. "Small sample multiple testing with application to cDNA microarray data." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4319.

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Many tests have been developed for comparing means in a two-sample scenario. Microarray experiments lead to thousands of such comparisons in a single study. Several multiple testing procedures are available to control experiment-wise error or the false discovery rate. In this dissertation, individual two-sample tests are compared based on accuracy, correctness, and power. Four multiple testing procedures are compared via simulation, based on data from the lab of Dr. Rajesh Miranda. The effect of sample size on power is also carefully examined. The two sample t-test followed by the Benjamini and Hochberg (1995) false discovery rate controlling procedure result in the highest power.
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Vesterlund, Jacob. "Feature Selection and Classification of cDNA Microarray Samples in ROSETTA." Thesis, Uppsala University, Department of Information Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-88731.

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The advent of cDNA microarray technology makes it possible to measure theexpression level of thousands of genes simultaneously. This creates large volumes of data that require computational analysis.

One application of microarray technology is cancer studies, where supervised learning may be used on microarray data to predicting tumour subtypes and other clinical parameters. The number of available objects (microarrays) is much smaller than the number of attributes (genes). Hence it is necessary to determine which attributes are important for predicting a parameter. Feature selection methods determine which parameters are related to the predicted parameter, and classifiers are then trained on data sets consisting only of the selected attributes.

This thesis examines the performance of several feature selection methods on real life data sets. The implementation is based on ROSETTA, a toolkit that contains several rough set learning algorithms as well as discretization methods, but lacks algorithms for performing features selection. These missing algorithms are written in the C++ programming language as part of this thesis.

The conclusion is that even though it appears to perform well in binary classification, the current implementation of multi-class classification does not perform as well the other methods studied as part of this thesis. If multi-class classification using binary classifiers and additional optimization was implemented, then it would be possible to compare the performance of rough set based classifiers to other method in a fair and meaningful way.

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Zhu, Ximin. "Topics on statistical design and analysis of cDNA microarray experiment." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/1206/.

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A microarray is a powerful tool for surveying the expression levels of many thousands of genes simultaneously. It belongs to the new genomics technologies which have important applications in the biological, agricultural and pharmaceutical sciences. In this thesis, we focus on the dual channel cDNA microarray which is one of the most popular microarray technologies and discuss three different topics: optimal experimental design; estimating the true proportion of true nulls, local false discovery rate (lFDR) and positive false discovery rate (pFDR) and dye effect normalization. The first topic consists of four subtopics each of which is about an independent and practical problem of cDNA microarray experimental design. In the first subtopic, we propose an optimization strategy which is based on the simulated annealing method to find optimal or near-optimal designs with both biological and technical replicates. In the second subtopic, we discuss how to apply Q-criterion for the factorial design of microarray experiments. In the third subtopic, we suggest an optimal way of pooling samples, which is actually a replication scheme to minimize the variance of the experiment under the constraint of fixing the total cost at a certain level. In the fourth subtopic, we indicate that the criterion for distant pair design is not proper and propose an alternative criterion instead. The second topic of this thesis is dye effect normalization. For cDNA microarray technology, each array compares two samples which are usually labelled with different dyes Cy3 and Cy5. It assumes that: for a given gene (spot) on the array, if Cy3-labelled sample has k times as much of a transcript as the Cy5-labelled sample, then the Cy3 signal should be k times as high as the Cy5 signal, and vice versa. This important assumption requires that the dyes should have the same properties. However, the reality is that the Cy3 and Cy5 dyes have slightly different properties and the relative efficiency of the dyes vary across the intensity range in a "banana-shape" way. In order to remove the dye effect, we propose a novel dye effect normalization method which is based on modeling dye response functions and dye effect curve. Real and simulated microarray data sets are used to evaluate the method. It shows that the performance of the proposed method is satisfactory. The focus of the third topic is the estimation of the proportion of true null hypotheses, lFDR and pFDR. In a typical microarray experiment, a large number of gene expression data could be measured. In order to find differential expressed genes, these variables are usually screened by a statistical test simultaneously. Since it is a case of multiple hypothesis testing, some kind of adjustment should be made to the p-values resulted from the statistical test. Lots of multiple testing error rates, such as FDR, lFDR and pFDR have been proposed to address this issue. A key related problem is the estimation of the proportion of true null hypotheses (i.e. non-expressed genes). To model the distribution of the p-values, we propose three kinds of finite mixture of unknown number of components (the first component corresponds to differentially expressed genes and the rest components correspond to non-differentially expressed ones). We apply a new MCMC method called allocation sampler to estimate the proportion of true null (i.e. the mixture weight of the first component). The method also provides a framework for estimating lFDR and pFDR. Two real microarray data studies plus a small simulation study are used to assess our method. We show that the performance of the proposed method is satisfactory.
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Zhao, Hongya. "Statistical analysis of gene expression data in cDNA microarray experiments." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/657.

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Kan, Takatsugu. "Gene expression profiling in human esophageal cancers using cDNA microarray." Kyoto University, 2003. http://hdl.handle.net/2433/148738.

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Books on the topic "CDNA microarray"

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Maziarz, Marlena. Spotting error in cDNA microarray data. Ottawa: National Library of Canada, 2003.

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Howell, Brandon George. Gene expression profiling of UV-induced skin cancer using cDNA microarray technology. Ottawa: National Library of Canada, 2001.

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Rintala, Nina. Differential gene expression in radiosensitive and radioresistant breast cancer cells using cDNA microarray analysis. Sudbury, Ont: Laurentian University, 2001.

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Liao, Wei-Yen Perry. Characterization of an HNF-3[beta] inducible construct and a cDNA microarray screen for HNF-3[beta] targets. Ottawa: National Library of Canada, 2003.

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Cerullo, Dante. Identification of a novel diagnostic marker for viral myocarditis using cDNA microarrays. Ottawa: National Library of Canada, 2003.

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Handley, Heather Martin. Zebrafish cardiovascular cDNA microarrays: Expression profiling and gene discovery in embryos exposed to 2,3,7,8-Tetrachlorodibenzo-P-dioxin. Cambridge, Mass: Massachusetts Institute of Technology, 2003.

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Book chapters on the topic "CDNA microarray"

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Mousses, Spyro. "Microarray (cDNA) Technology." In Encyclopedia of Cancer, 1–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27841-9_3710-3.

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Mousses, Spyro. "Microarray (cDNA) Technology." In Encyclopedia of Cancer, 2821–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-46875-3_3710.

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Mousses, Spyro. "Microarray (cDNA) Technology." In Encyclopedia of Cancer, 2289–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16483-5_3710.

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Rantala-Ylinen, Anne, Kaarina Sivonen, Annick Wilmotte, Hans C. P. Matthijs, and J. Merijn Schuurmans. "DNA (Diagnostic) and cDNA Microarray." In Molecular Tools for the Detection and Quantification of Toxigenic Cyanobacteria, 241–61. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119332169.ch8.

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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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Larese, Mónica G., and Juan Carlos Gómez. "Quantitative Improvements in cDNA Microarray Spot Segmentation." In Advances in Bioinformatics and Computational Biology, 60–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03223-3_6.

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Wong, Kwong-Kwok, Jun Kanno, Rita Cheng, Lyle Sasser, James Morris, Larry Anderson, Joel Pounds, and Tohru Inoue. "Application of cDNA microarray for uterotrophic assay." In Toxicogenomics, 141–48. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-66999-9_18.

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Yang, Yee Hwa, and Natalie P. Thorne. "Normalization for two-color cDNA microarray data." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 403–18. Beachwood, OH: Institute of Mathematical Statistics, 2003. http://dx.doi.org/10.1214/lnms/1215091155.

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Laere, Steven J. Van, Peter B. Vermeulen, and Luc Y. Dirix. "cDNA Microarray Analysis of Inflammatory Breast Cancer Signatures." In Methods in Molecular Biology™, 71–98. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-530-9_6.

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Weng, Guirong, Yijun Hu, and Zhiyao Li. "cDNA Microarray Image Filtering Using Shape-Adaptive DCT." In Recent Advances in Computer Science and Information Engineering, 109–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25792-6_17.

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Conference papers on the topic "CDNA microarray"

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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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Liew, Alan Wee-Chung, Hong Yan, Tuan D. Pham, and Xiaobo Zhou. "Automated cDNA Microarray Image Segmentation." In COMPUTATIONAL MODELS FOR LIFE SCIENCES/CMLS '07. AIP, 2007. http://dx.doi.org/10.1063/1.2816637.

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Pan, Youlian, Jitao Zou, Yi Huang, Ziying Liu, Sieu Phan, and Fazel A. Famili. "Goal Driven Analysis of cDNA Microarray Data." In 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2009. http://dx.doi.org/10.1109/cibcb.2009.4925727.

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Chan, Shih-Huang, Wan-Chi Chang, and Chien-Ju Lin. "Contamination removal methods in cDNA microarray data." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353145.

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Kaabouch, Naima, and Hamid Shahbazkia. "Automatic techniques for gridding CDNA microarray images." In 2008 IEEE International Conference on Electro/Information Technology (EIT 2008). IEEE, 2008. http://dx.doi.org/10.1109/eit.2008.4554300.

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Lukac, Rastislav, and Konstantinos Plataniotis. "cDNA Microarray Imaging using Single-Sensor Technology." In 2006 Canadian Conference on Electrical and Computer Engineering. IEEE, 2006. http://dx.doi.org/10.1109/ccece.2006.277841.

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Yao, Zhang, and Wu Shunxiang. "Statistsics-Adaptive Method for cDNA Microarray Images Gridding." In 2012 4th International Conference on Digital Home (ICDH). IEEE, 2012. http://dx.doi.org/10.1109/icdh.2012.52.

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Uslan, V., and İ Ö Bucak. "Clustering-based spot segmentation of cDNA microarray images." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626430.

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Weng, Guirong, Yuehuan Wang, and Jian Su. "cDNA Microarray Image Processing Using Spot Centroid of Intensity." In 2008 International Conference on Computational Intelligence and Security (CIS). IEEE, 2008. http://dx.doi.org/10.1109/cis.2008.31.

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Joseph, Steffy Maria, and P. S. Sathidevi. "cDNA Microarray Image Enhancement for Effective Gridding of Spots." In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929512.

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Reports on the topic "CDNA microarray"

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Jornsten, Rebeka, Bin Yu, Wei Wang, and Kannan Ramchandran. Compression of cDNA and Inkjet Microarray Images. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada407645.

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Rosa, Artur J. M., Gang Ren, and James M. Reecy. Development of the BoviAnalyser cDNA Bovine Microarray. Ames (Iowa): Iowa State University, January 2004. http://dx.doi.org/10.31274/ans_air-180814-592.

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Gottardo, Raphael, Adrian E. Raftery, Ka Yee Yeung, and Roger E. Bumgarner. Robust Estimation of cDNA Microarray Intensities with Replicates. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459797.

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Godwin, Andrew K. Identification of Candidate Breast Cancer Susceptibility Genes Using a cDNA Microarray/CGH Approach. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada408112.

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Godwin, Andrew K. Identification of Candidate Breast Cancer Susceptibility Genes Using a cDNA Microarray/CGH Approach. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada417397.

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Huang, Shixia, and Harold Varmus. The Use of cDNA Microarray to Study Gene Expression in Wnt-1 Induced Mammary Tumors. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada411264.

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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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Porat, Ron, Gregory T. McCollum, Amnon Lers, and Charles L. Guy. Identification and characterization of genes involved in the acquisition of chilling tolerance in citrus fruit. United States Department of Agriculture, December 2007. http://dx.doi.org/10.32747/2007.7587727.bard.

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Citrus, like many other tropical and subtropical fruit are sensitive to chilling temperatures. However, application of a pre-storage temperature conditioning (CD) treatment at 16°C for 7 d or of a hot water brushing (HWB) treatment at 60°C for 20 sec remarkably enhances chilling tolerance and reduces the development of chilling injuries (CI) upon storage at 5°C. In the current research, we proposed to identify and characterize grapefruit genes that are induced by CD, and may contribute to the acquisition of fruit chilling tolerance, by two different molecular approaches: cDNA array analysis and PCR cDNA subtraction. In addition, following the recent development and commercialization of the new Affymetrix Citrus Genome Array, we further performed genome-wide transcript profiling analysis following exposure to CD and chilling treatments. To conduct the cDNA array analysis, we constructed cDNA libraries from the peel tissue of CD- and HWB-treated grapefruit, and performed an EST sequencing project including sequencing of 3,456 cDNAs from each library. Based on the obtained sequence information, we chose 70 stress-responsive and chilling-related genes and spotted them on nylon membranes. Following hybridization the constructed cDNA arrays with RNA probes from control and CD-treated fruit and detailed confirmations by RT-PCR analysis, we found that six genes: lipid-transfer protein, metallothionein-like protein, catalase, GTP-binding protein, Lea5, and stress-responsive zinc finger protein, showed higher transcript levels in flavedo of conditioned than in non-conditioned fruit stored at 5 ᵒC. The transcript levels of another four genes: galactinol synthase, ACC oxidase, temperature-induced lipocalin, and chilling-inducible oxygenase, increased only in control untreated fruit but not in chilling-tolerant CD-treated fruit. By PCR cDNA subtraction analysis we identified 17 new chilling-responsive and HWB- and CD-induced genes. Overall, characterization of the expression patterns of these genes as well as of 11 more stress-related genes by RNA gel blot hybridizations revealed that the HWB treatment activated mainly the expression of stress-related genes(HSP19-I, HSP19-II, dehydrin, universal stress protein, EIN2, 1,3;4-β-D-glucanase, and SOD), whereas the CD treatment activated mainly the expression of lipid modification enzymes, including fatty acid disaturase2 (FAD2) and lipid transfer protein (LTP). Genome wide transcriptional profiling analysis using the newly developed Affymetrix Citrus GeneChip® microarray (including 30,171 citrus probe sets) revealed the identification of three different chilling-related regulons: 1,345 probe sets were significantly affected by chilling in both control and CD-treated fruits (chilling-response regulon), 509 probe sets were unique to the CD-treated fruits (chilling tolerance regulon), and 417 probe sets were unique to the chilling-sensitive control fruits (chilling stress regulon). Overall, exposure to chilling led to expression governed arrest of general cellular metabolic activity, including concretive down-regulation of cell wall, pathogen defense, photosynthesis, respiration, and protein, nucleic acid and secondary metabolism. On the other hand, chilling enhanced various adaptation processes, such as changes in the expression levels of transcripts related to membranes, lipid, sterol and carbohydrate metabolism, stress stimuli, hormone biosynthesis, and modifications in DNA binding and transcription factors.
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Lichter, Amnon, Gopi K. Podila, and Maria R. Davis. Identification of Genetic Determinants that Facilitate Development of B. cinerea at Low Temperature and its Postharvest Pathogenicity. United States Department of Agriculture, March 2011. http://dx.doi.org/10.32747/2011.7592641.bard.

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Abstract:
Botrytis cinerea is the postharvest pathogen of many agricultural produce with table grapes, strawberries and tomatoes as major targets. The high efficiency with which B. cinerea causes disease on these produce during storage is attributed in part due to its exceptional ability to develop at very low temperature. Our major goal was to understand the genetic determinants which enable it to develop at low temperature. The specific research objectives were: 1. Identify expression pattern of genes in a coldenriched cDNA library. 2. Identify B. cinerea orthologs of cold-induced genes 3. Profile protein expression and secretion at low temperature on strawberry and grape supplemented media. 4. Test novel methods for the functional analysis of coldresponsive genes. Objective 1 was modified during the research because a microarray platform became available and it allowed us to probe the whole set of candidate genes according to the sequence of 2 strains of the fungus, BO5.10 and T4. The results of this experiment allowed us to validate some of our earlier observations which referred to genes which were the product of a SSH suppression-subtraction library. Before the microarray became available during 2008 we also analyzed the expression of 15 orthologs of cold-induced genes and some of these results were also validated by the microarray experiment. One of our goals was also to perform functional analysis of cold-induced genes. This goal was hampered for 3 years because current methodology for transformation with ‘protoplasts’ failed to deliver knockouts of bacteriordopsin-like (bR) gene which was our primary target for functional analysis. Consequently, we developed 2 alternative transformation platforms, one which involves an air-gun based technique and another which involves DNA injection into sclerotia. Both techniques show great promise and have been validated using different constructs. This contribution is likely to serve the scientific community in the near future. Using these technologies we generated gene knockout constructs of 2 genes and have tested there effect on survival of the fungus at low temperature. With reference to the bR genes our results show that it has a significant effect on mycelial growth of the B. cinerea and the mutants have retarded development at extreme conditions of ionic stress, osmotic stress and low temperature. Another gene of unknown function, HP1 is still under analysis. An ortholog of the yeast cold-induced gene, CCH1 which encodes a calcium tunnel and was shown to be cold-induced in B. cinerea was recently cloned and used to complement yeast mutants and rescue them from cold-sensitivity. One of the significant findings of the microarray study involves a T2 ribonuclease which was validated to be cold-induced by qPCR analysis. This and other genes will serve for future studies. In the frame of the study we also screened a population of 631 natural B. cinerea isolates for development at low temperature and have identified several strains with much higher and lower capacity to develop at low temperature. These strains are likely to be used in the future as candidates for further functional analysis. The major conclusions from the above research point to specific targets of cold-induced genes which are likely to play a role in cold tolerance. One of the most significant observations from the microarray study is that low temperature does not induce ‘general stress response in B. cinerea, which is in agreement to its exceptional capacity to develop at low temperature. Due to the tragic murder of the Co-PI Maria R. Davis and GopiPodila on Feb. 2010 it is impossible to deliver their contribution to the research. The information of the PI is that they failed to deliver objective 4 and none of the information which relates to objective 3 has been delivered to the PI before the murder or in a visit to U. Alabama during June, 2010. Therefore, this report is based solely on the IS data.
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10

Gottardo, Raphael, Adrian E. Raftery, Ka Y. Yeung, and Roger E. Bumgarner. Bayesian Robust Inference for Differential Gene Expression in cDNA Microarrays with Multiple Samples. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada478418.

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