Дисертації з теми "Leukaemia; microarray; gene expression"
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com, Darcelle@gmail, and Darcelle Natalie Dixon. "Identification of Downstream Target Genes of the T-cell Oncoprotein HOX11 by Global Gene Expression Profiling." Murdoch University, 2004. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20040929.143814.
Повний текст джерелаLoi, To Ha Clinical School St Vincent's Hospital Faculty of Medicine UNSW. "Gene expression profiling in Philadelphia positive acute lymphoblastic leukaemia treated with Imatinib -- a novel role of PKC epsilon signalling." Publisher:University of New South Wales. Clinical School - St Vincent's Hospital, 2008. http://handle.unsw.edu.au/1959.4/43343.
Повний текст джерелаQuinn, M. F. "Homeobox gene expression in acute leukaemia." Thesis, Queen's University Belfast, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398094.
Повний текст джерелаSzeto, Lap Keung. "Clustering analysis of microarray gene expression data /." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-it-b19885817a.pdf.
Повний текст джерела"Submitted to Department of Computer Engineering and Information Technology in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 70-79)
Botella, Pérez Cristina. "Multivariate classification of gene expression microarray data." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/9046.
Повний текст джерелаEijssen, Lars Maria Theo. "Analysis of microarray gene expression data sets." [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Universiteit Maastricht [host], 2006. http://arno.unimaas.nl/show.cgi?fid=6830.
Повний текст джерелаMolloy, Timothy John St George Clinical School UNSW. "Gene expression in healing tendon." Awarded by:University of New South Wales. St George Clinical School, 2006. http://handle.unsw.edu.au/1959.4/23939.
Повний текст джерелаLaurell, Cecilia. "Microarray Based Gene Expression Analysis in Cancer Research." Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4244.
Повний текст джерелаMohammed, Suhaib. "Consensus network inference of microarray gene expression data." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/24185.
Повний текст джерелаMorimoto, Shoko. "Global Gene Expression in Haloferax volcanii." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306873403.
Повний текст джерелаJiang, Ying, and 蔣穎. "Studies of gene regulation using microarray data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29976388.
Повний текст джерелаKhondoker, Md Mizanur Rahman. "Statistical methods for pre-processing microarray gene expression data." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/12367.
Повний текст джерелаAmin, Shilu. "Epigenetic regulation of ZAP70 gene expression in chronic lymphocytic leukaemia." Thesis, University of Newcastle Upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.512133.
Повний текст джерелаMarconi, Daniela <1979>. "New approaches to open problems in gene expression microarray data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/842/.
Повний текст джерелаZhu, Yitan. "Learning Statistical and Geometric Models from Microarray Gene Expression Data." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28924.
Повний текст джерелаPh. D.
Zhang, Guilin. "Clustering Algorithms for Time Series Gene Expression in Microarray Data." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc177269/.
Повний текст джерелаZhao, Hongya. "Statistical analysis of gene expression data in cDNA microarray experiments." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/657.
Повний текст джерелаKan, Takatsugu. "Gene expression profiling in human esophageal cancers using cDNA microarray." Kyoto University, 2003. http://hdl.handle.net/2433/148738.
Повний текст джерелаWood, Angela Clare. "Expression of the HOX A gene cluster in acute myeloid leukaemia." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300219.
Повний текст джерелаBosio, Mattia. "Hierarchical information representation and efficient classification of gene expression microarray data." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/145902.
Повний текст джерелаEn el campo de la biología computacional, los microarrays son utilizados para medir la actividad de miles de genes a la vez y producir una representación global de la función celular. Los microarrays permiten analizar la expresión de muchos genes en un solo experimento, rápidamente y eficazmente. Aunque los microarrays sean una tecnología de investigación consolidada hoy en día y la tendencia es en utilizar nuevas tecnologías como Next Generation Sequencing (NGS), aun no se ha encontrado un método óptimo para la clasificación de muestras. La clasificación de muestras de microarray es una tarea complicada, debido al alto número de variables y a la falta de estructura entre los datos. Esta característica impide la aplicación de técnicas de procesado que se basan en relaciones estructurales, como el filtrado con wavelet u otras técnicas de filltrado. Por otro lado, los genes no se expresen independientemente unos de otros: los genes están inter-relacionados según el proceso biológico que les regula. El objetivo de esta tesis es mejorar el estado del arte en la clasi cación de microarrays y contribuir a entender cómo se pueden diseñar y aplicar técnicas de procesado de señal para analizar microarrays. El objetivo de construir un algoritmo de clasi cación, necesita un estudio de comprobaciones y adaptaciones de algoritmos existentes a los datos analizados. Los algoritmo desarrollados en esta tesis encaran el problema con dos bloques esenciales. El primero ataca la falta de estructura, derivando un árbol binario usando herramientas de clustering no supervisado. El segundo elemento fundamental para obtener clasificadores precisos reduciendo el riesgo de overfitting es un elemento de selección de variables. La principal tarea en esta tesis es la clasificación de datos binarios en la cual hemos obtenido mejoras relevantes al estado del arte. El primer paso es la generación de una estructura, para eso se ha utilizado el algoritmo Treelets disponible en la literatura. Múltiples alternativas a este algoritmo original han sido propuestas y evaluadas, cambiando las métricas de similitud o las reglas de fusión durante el proceso. Además, se ha estudiado la posibilidad de usar fuentes de información externas, como ontologías de información biológica, para mejorar la inferencia de la estructura. Se han estudiado dos enfoques diferentes para la selección de variables: el primero es una modificación del algoritmo IFFS y el segundo utiliza un esquema de aprendizaje con “ensembles”. El algoritmo IFFS ha sido adaptado a las características de microarrays para obtener mejores resultados, añadiendo elementos como la medida de fiabilidad y un sistema de evaluación para seleccionar la mejor variable en cada iteración. El método que utiliza “ensembles” aprovecha la abundancia de features de los microarrays para implementar una selección diferente. En este campo se han estudiado diferentes algoritmos, mejorando alternativas ya existentes al escaso número de muestras y al alto número de variables, típicos de los microarrays. El problema de clasificación con más de dos clases ha sido también tratado al estudiar un nuevo algoritmo que combina múltiples clasificadores binarios. El algoritmo propuesto aprovecha la redundancia ofrecida por múltiples clasificadores para obtener predicciones más fiables. Todos los algoritmos propuestos en esta tesis han sido evaluados con datos públicos y de alta calidad, siguiendo protocolos establecidos en la literatura para poder ofrecer una comparación fiable con el estado del arte. Cuando ha sido posible, se han aplicado simulaciones Monte Carlo para mejorar la robustez de los resultados.
Jernås, Margareta. "Microarray analysis of gene expression in human adipocytes and adipose tissue /." Göteborg : Institute of Medicine, Dept. of Molecular and Clinical Medicine, Sahlgrenska Academy, Göteborg University, 2008. http://hdl.handle.net/2077/9583.
Повний текст джерелаHong, Fangxin. "Statistical methods for analysis of microarray time course gene expression data /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2004. http://uclibs.org/PID/11984.
Повний текст джерелаLeng, Xiaoyan. "Functional discriminant analysis and time dynamics of microarray gene co-expression /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2004. http://uclibs.org/PID/11984.
Повний текст джерелаFält, Susann. "Analysis of global gene expression in complex biological systems using microarray technology /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-612-3/.
Повний текст джерелаLim, Sanghyun. "Sorghum gene expression modulated by water deficit and cold stress." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4705.
Повний текст джерелаLili, Loukia. "Computational analyses of gene expression profiles of ovarian and pancreatic cancer." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52911.
Повний текст джерелаThe data related to the ovarian cancer work discussed in this dissertation are available at: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38666
Gabbur, Prasad. "Machine Learning Methods for Microarray Data Analysis." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195829.
Повний текст джерелаZhu, Yong-Ming. "Studies on expression of tumour suppressor genes in acute myeloblastic leukaemia." Thesis, Nottingham Trent University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297012.
Повний текст джерелаStelios, Pavlidis. "Pathway based microarray analysis based on multi-membership gene regulation." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6968.
Повний текст джерелаHsu, Jessie. "Outcome-Driven Clustering of Microarray Data." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10410.
Повний текст джерелаKhamesipour, Alireza. "IMPROVED GENE PAIR BIOMARKERS FOR MICROARRAY DATA CLASSIFICATION." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1573.
Повний текст джерелаHasan, Mohammad Shabbir. "Investigating Gene Relationships in Microarray Expressions: Approaches Using Clustering Algorithms." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1376536496.
Повний текст джерелаKirk, Michael School of Biotechnology & Biomolecular Science UNSW. "Bioinformatic analyses of microarray experiments on genetic control of gene expression level." Awarded by:University of New South Wales. School of Biotechnology and Biomolecular Science, 2006. http://handle.unsw.edu.au/1959.4/25986.
Повний текст джерелаHowell, Brandon George. "Gene expression profiling of UV-induced skin cancer using cDNA microarray technology." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63108.pdf.
Повний текст джерелаTurro, Ernest. "Statistcal methods for gene expression analysis using microarray and RNA-Seq data." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.534964.
Повний текст джерелаDvergsten, Erik C. "A Weighted Gene Co-expression Network Analysis for Streptococcus sanguinis Microarray Experiments." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4430.
Повний текст джерелаYamaga, Yuichi. "Gene expression profile of Dclk1+ cells in intestinal tumors." Kyoto University, 2019. http://hdl.handle.net/2433/236595.
Повний текст джерелаChang, Xiaoqing. "Bayesian Mixtures and Gene Expression Profiling with Missing Data." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1226090856.
Повний текст джерелаWennmalm, Kristian. "Analytical strategies for identifying relevant phenotypes in microarray data /." Stockholm, 2007. http://diss.kib.ki.se/2007/978-91-7357-401-3/.
Повний текст джерелаYuan, Lihui. "Quorum sensing regulated gene expression in Porphyromonas gingivalis." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0010043.
Повний текст джерелаTypescript. Title from title page of source document. Document formatted into pages; contains 134 pages. Includes Vita. Includes bibliographical references.
Wu, Meng. "Data mining cDNA microarray experiment with a GEE approach /." Electronic version (PDF), 2004. http://dl.uncw.edu/etd/2004/wum/mengwu.pdf.
Повний текст джерелаNguyen, Phuong Minh Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "A new normalized EM algorithm for clustering gene expression data." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/43261.
Повний текст джерелаBall, Sarah Elizabeth. "A study of c-fms in myeloid leukaemias." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252976.
Повний текст джерелаWang, Tao. "Statistical design and analysis of microarray experiments." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.
Повний текст джерелаTitle 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
Falin, Lee J. "Systems Uncertainty in Systems Biology & Gene Function Prediction." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26634.
Повний текст джерелаPh. D.
Andersson, Tove. "Approaches to differential gene expression analysis in atherosclerosis." Doctoral thesis, KTH, Biotechnology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3400.
Повний текст джерелаTodays rapid development of powerful tools for geneexpression analysis provides unprecedented resources forelucidating complex molecular events.
The objective of this workhas been to apply, combine andevaluate tools for analysis of differential gene expressionusing atherosclerosis as a model system. First, an optimisedsolid-phase protocol for representational difference analysis(RDA) was applied to twoin vitromodel systems. Initially, The RDA enrichmentprocedure was investigated by shotgun cloning and sequencing ofsuccessive difference products. In the subsequent steps,combinations of RDA and microarray analysis were used tocombine the selectivity and sensitivity of RDA with thehigh-throughput nature of microarrays. This was achieved byimmobilization of RDA clones onto microarrays dedicated forgene expression analysis in atherosclerosis as well ashybridisation of labelled RDA products onto global microarrayscontaining more than 32,000 human clones. Finally, RDA wasapplied for the investigation of the focal localisation ofatherosclerotic plaques in mice usingin vivotissue samples as starting material.
A large number of differentially expressed clones wereisolated and confirmed by real time PCR. A very diverse rangeof gene fragments was identified in the RDA products especiallywhen they were screened with global microarrays. However, themicroarray data also seem to contain some noise which is ageneral problem using microarrays and should be compensated forby careful verification of the results.
Quite a large number of candidate genes related to theatherosclerotic process were found by these studies. Inparticular several nuclear receptors with altered expression inresponse to oxidized LDL were identified and deserve furtherinvestigation. Extended functional annotation does not liewithin the scope of this thesis but raw data in the form ofnovel sequences and accession numbers of known sequences havebeen made publicly available in GenBank. Parts of the data arealso available for interactive exploration on-line through aninteractive software tool. The data generated thus constitute abase for new hypotheses to be tested in the field ofatherosclerosis.
Keywords:representational difference analysis, geneexpression profiling, microarray analysis, atherosclerosis,foam cell formation
章明明 and Ming-ming Cheung. "An examination of the regulation of gene expression using microarray and genomic resources." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31225809.
Повний текст джерелаPerera, Suriya Arachchige Chandrika Nishanthi. "Fine mapping of QTL and microarray gene expression studies in arabidopsis using STAIRS." Thesis, University of Birmingham, 2005. http://etheses.bham.ac.uk//id/eprint/1652/.
Повний текст джерелаXiang, Lianbin, Katalin Szebeni, Craig A. Stockmeier, Samuel S. Newton, and Gregory A. Ordway. "Microarray Analysis of Gene Expression in the Noradrenergic Locus Coeruleus in Major Depression." Digital Commons @ East Tennessee State University, 2006. https://dc.etsu.edu/etsu-works/8621.
Повний текст джерелаShen, Yijing. "Study of functionally related gene groups using microarray expression data theory and application /." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1679380101&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Повний текст джерела