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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.
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HOX11 is a homeodomain transcription factor that has been implicated in leukaemic transformation associated with T-cell acute lymphoblastic leukaemia (T-ALL). Its role in leukaemogenesis remains enigmatic, nevertheless, in vitro and in vivo studies have provided additional evidence supporting the role of HOX11 as an oncogene. The mechanism by which HOX11 transforms cells is yet to be elucidated, however, HOX11 has been postulated to function by binding regulatory elements within the promoter regions of specific target genes in order to control gene transcription. The identification of transcriptional targets is thus thought to be critical to our understanding of the pathways controlled by this master gene regulator. To date, only three candidate HOX11 target genes have been reported and given that HOX11 overexpression can have a profound impact on cell behaviour, it is likely that many more exist. In this study, we sought to further understand the role of HOX11 in tumorigenesis by: 1) The identification of novel putative HOX11 target genes by profiling gene expression in response to HOX11 in a number of cell lines using a combination of RDA, cDNA microarray and GeneChip approaches and 2) confirming target gene status by assessing whether the proximal promoters of the leading candidates identified are transcriptionally regulated by HOX11.
To identify genes whose expression was altered by HOX11, three techniques were employed, namely representational difference analysis, cDNA microarray and Affymetrix GeneChip array. Because of the relative novelty of these technologies, all three methods were employed in a complementary manner. While representational difference analysis did not require dedicated equipment and enabled the identification of novel genes, the technique was labour-intensive and also exhibited a number of problems including high levels of background. Emphasis was therefore placed on the more systematic microarray approaches that enabled a global investigation of expression patterns and thus the identification of a range of candidate target genes. Initially, this involved cDNA microarray experiments, however, during the course of this work Affymetrix GeneChip technology became available. The latter was identified as the most appropriate technology for the identification of candidate target genes because of its relative ease of use, as well as its employment of multiple independent probe pairs which greatly improved background noise, increased the range and accuracy of detection, minimized the effects of cross hybridization and drastically reduced the rate of false positives and miscalls.
Using these combined approaches, several genes of interest were identified which were differentially regulated in the presence of HOX11 and thus may represent oncogenically or physiologically relevant target genes. These included OSTEOPONTIN, PAG, GUANOSINE DIPHOSPHATE DISSOCIATION INHIBITOR 3, SUR8, GAS3, C-KIT, VEGFC, NOR1 and SMARCD3. In order to confirm their role as target genes, four candidates (C-KIT, VEGFC, NOR1 and SMARCD3) were characterized in terms of the ability of their proximal promoters to be transcriptionally regulated by HOX11 using luciferase reporter assays. Significant repression of the proximal promoters of C-KIT and VEGFC by HOX11 was observed, which provided further evidence for their status as target genes. This repression was, however, in stark contrast to the transcriptional activation seen when the C-KIT and VEGFC proximal promoters were co-transfected with a HOX11 mutant lacking the third helix of the DNA-binding homeodomain. This unexpected finding suggested that the transcriptional activity of HOX11 is complex and highly context-dependent, and in particular, highlighted the importance of an intact homeodomain for HOX11 function.
C-KIT and VEGFC are both involved in tyrosine kinase signal transduction pathways, as a receptor tyrosine kinase and tyrosine kinase ligand, respectively. C-KIT plays an important role in the survival and self-renewal of haematopoietic cells. It is a previously identified and relatively well characterized oncogene known to be regulated by other transcription factors (SCL/TAL1 and LMO) implicated in the pathogenesis of T-ALL. VEGFC is a member of the vascular endothelial growth factor family that functions in angiogenesis and lymphangiogenesis. A paracrine loop involving VEGFC and its receptor VEGFR-3 has previously been implicated in leukaemic cell survival. While further work is required in order to confirm the status of VEGFC and C-KIT as oncogenically-relevant HOX11 target genes and to characterize their exact mode of regulation, these findings implicate receptor tyrosine kinases in HOX11-mediated tumorigenesis and underscore their potential importance as therapeutic targets in haematological malignancies.
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Loi, 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.
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Philadelphia positive (Ph+) Acute Lymphoblastic Leukaemia (ALL) is characterised by the presence of the BCR-ABL fusion gene, which encodes a protein tyrosine kinase with aberrant activity. Imatinib, a chemical Bcr-Abl inhibitor, is rarely effective in Ph+ ALL patients as a single agent. In this study, insight into molecular and signalling changes occurring in Ph+ ALL during Imatinib therapy were investigated using cDNA microarrays. An optimal microarray assay was established to examine the gene expression changes in leukaemic cells from Ph+ ALL patients treated with Imatinib. Over 500 genes with ≥1.5-fold up- or down-regulation were identified. Based on gene ontology and novelty to Bcr-Abl signalling, six genes were selected and expression changes in five of these genes (PKCε, PINK1, SPRY2, ATF4 and PECAM1) confirmed by real time RT-PCR in Imatinib treated primary Ph+ ALL cells or the SUP-B15 cell line. The functional role of Protein Kinase C epsilon (PKCε) in response to Imatinib was further investigated using the Ph+ lymphoid and myeloid cell lines, SUP-B15 and K562. Detection of Imatinib-induced apoptosis by annexin V and PI staining demonstrated that SUP-B15 cells were less sensitive to Imatinib compared to K562 cells. PKCε mRNA was 50-fold higher in Ph+ ALL cells than Ph+ myeloid cells. In SUP-B15 cells, Imatinib upregulated PKCε mRNA but the protein was reduced by proteolytic cleavage. Inhibition of caspases showed that this cleaved product was not required for Imatinib induced-apoptosis. The treatment of SUP-B15 and primary Ph+ ALL cells with TAT-εV1-2 peptide, a specific inhibitor of PKCε, increased Imatinib-induced apoptosis. While the forced overexpression of PKCε in K562 cells reduced Imatinib-induced apoptosis. This increased expression of PKCε was associated with the increase of survival and anti-apoptotic proteins, Akt and Bcl-2. In summary, Gene expression profiling of Ph+ ALL cells during Imatinib therapy identified PKCε as an Imatinib responsive gene. A novel role of PKCε in Ph+ ALL response to imatinib is proposed. Experimental data presented in this thesis indicate that PKCε mediates pro-survival/anti-apoptosis signals in Ph+ ALL thereby reducing Imatinib-induced death. Thus, targeting PKCε during Imatinib therapy may be beneficial for the future treatment of Ph+ ALL.
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Quinn, 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.
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Szeto, 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.
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Thesis (M.Phil.)--City University of Hong Kong, 2005."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)
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Botella, Pérez Cristina. "Multivariate classification of gene expression microarray data." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/9046.
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L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal.
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Eijssen, 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.
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Molloy, 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.
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Tendon injury is painful and often debilitating, and is a one of the most prevalent soft tissue injuries encountered in the clinic. While common, the underlying molecular and genetic processes of tendon damage and repair remain poorly understood. The work described herein used genome-wide expression analyses to investigate tendon injury and healing from three perspectives. The first identified novel gene expression in tendon fibroblasts following their stimulation with nitric oxide (NO). Of particular relevance to tendon healing was the observation that stimulated fibroblasts express a number of extracellular matrix (ECM) genes in response to NO in a dose-dependent manner, and that NO significantly affects cellular adhesion, a critical process during tendon repair. These findings will be of use when optimising dosages of NO delivery in future work investigating NO as potential treatment for tendon injuries. The second study examined gene expression in an acute tendon injury model in the rat at 1, 7, and 21 days post injury, roughly representing the inflammation, proliferation, and remodelling phase of wound repair. Several novel genes and pathways were found to be differentially expressed at each stage of healing. Of particular interest were the presence of a significant number of genes related to glutamate signaling, a method of cellular communication that has not previously been shown to exist in tendon. Also upregulated were a number of genes which have previously only been associated with embryonic development. Finally, gene expression in a supraspinatus tendinopathy model in the rat was investigated. Several genetic pathways were identified in tendinopathic tendons which have not previously been associated with the disease, and, analogous to the acute injury model study, glutamate signaling gene overexpression was also prevalent. Further in vitro studies showed that the expression of these genes in tendon fibroblasts were stimulated by glutamate treatment, which in turn upregulated pro-apoptotic pathways causing cell death. This may prove to be an important causative factor in the tendon degeneration seen in tendinopathy, in which apoptosis has been identified as playing a significant role. The results of these studies contribute to a better understanding of the aetiology of several extremely common pathologies of this soft tissue, and may help to develop more targeted therapies for increasing the efficacy of tendon healing in future.
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Laurell, 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.
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Mohammed, Suhaib. "Consensus network inference of microarray gene expression data." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/24185.
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Genetic and protein interactions are essential to regulate cellular machinery. Their identification has become an important aim of systems biology research. In recent years, a variety of computational network inference algorithms have been employed to reconstruct gene regulatory networks from post-genomic data. However, precisely predicting these regulatory networks remains a challenge. We began our study by assessing the ability of various network inference algorithms to accurately predict gene regulatory interactions using benchmark simulated datasets. It was observed from our analysis that different algorithms have strengths and weaknesses when identifying regulatory networks, with a gene-pair interaction (edge) predicted by one algorithm not always necessarily consistent with the other. An edge not predicted by most inference algorithms may be an important one, and should not be missed. The naïve consensus (intersection) method is perhaps the most conservative approach and can be used to address this concern by extracting the edges consistently predicted across all inference algorithms; however, it lacks credibility as it does not provide a quantifiable measure for edge weights. Existing quantitative consensus approaches, such as the inverse-variance weighted method (IVWM) and the Borda count election method (BCEM), have been previously implemented to derive consensus networks from diverse datasets. However, the former method was biased towards finding local solutions in the whole network, and the latter considered species diversity to build the consensus network. In this thesis we proposed a novel consensus approach, in which we used Fishers Combined Probability Test (FCPT) to combine the statistical significance values assigned to each network edge by a number of different networking algorithms to produce a consensus network. We tested our method by applying it to a variety of in silico benchmark expression datasets of different dimensions and evaluated its performance against individual inference methods, Bayesian models and also existing qualitative and quantitative consensus techniques. We also applied our approach to real experimental data from the yeast (S. cerevisiae) network as this network has been comprehensively elucidated previously. Our results demonstrated that the FCPT-based consensus method outperforms single algorithms in terms of robustness and accuracy. In developing the consensus approach, we also proposed a scoring technique that quantifies biologically meaningful hierarchical modular networks.
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Morimoto, Shoko. "Global Gene Expression in Haloferax volcanii." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306873403.
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Jiang, 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.
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Khondoker, Md Mizanur Rahman. "Statistical methods for pre-processing microarray gene expression data." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/12367.
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A novel method is developed for combining multiple laser scans of microarrays to correct for “signal saturation” and “signal deterioration” effects in the gene expression measurement. A multivariate nonlinear functional regression model with Cauchy distributed errors having additive plus multiplicative scale is proposed as a model for combining multiple scan data. The model has been found to flexibly describe the nonlinear relationship in multiple scan data. The heavy tailed Cauchy distribution with additive plus multiplicative scale provides a basis for objective and robust estimation of gene expression from multiple scan data adjusting for censoring and deterioration bias in the observed intensity. Through combining multiple scans, the model reduces sampling variability in the gene expression estimates. A unified approach for nonparametric location and scale normalisation of log-ratio data is considered. A Generalised Additive Model for Location, Scale and Shape (GAMLSS) is proposed. GAMLSS uses a nonparametric approach for modelling both location and scale of log-ratio data, in contrast to the general tendency of using a parametric transformation, such as arcsinh, for variance stabilisation. Simulation studies demonstrate GAMLSS to be more powerful than the parametric method when a GAMLSS location and scale model, fitted to real data, is assumed correct. GAMLSS has been found to be as powerful as the parametric approach even when the parametric model is appropriate. Finally, we investigate the optimality of different estimation methods for analysing functional regression models. Alternative estimators are available in the literature to deal with the problems of identifiability and consistency. We investigated these estimators in terms of unbiasedness and efficiency for a specific case involving multiple laser scans of microarrays, and found that, in addition to being consistent, named methods are highly efficient and unbiased.
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Amin, 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.
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Marconi, 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/.
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In the past decade, the advent of efficient genome sequencing tools and high-throughput
experimental biotechnology has lead to enormous progress in the life science. Among
the most important innovations is the microarray tecnology. It allows to quantify the
expression for thousands of genes simultaneously by measurin the hybridization from a
tissue of interest to probes on a small glass or plastic slide. The characteristics of these
data include a fair amount of random noise, a predictor dimension in the thousand, and
a sample noise in the dozens.
One of the most exciting areas to which microarray technology has been applied is
the challenge of deciphering complex disease such as cancer. In these studies, samples are
taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to
reach a standard organization (through the effort of preposed International project like
Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble
in a clinician's question that do not have a compelling statistical method that could
permit to answer it.The contribution of this dissertation in deciphering disease regards
the development of new approaches aiming at handle open problems posed by clinicians
in handle specific experimental designs.
In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the
production of the array, to the quality controls ending with preprocessing steps that will
be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical
review of standard analysis methods are provided stressing most of problems that
In Chapter 3 is introduced a method to adress the issue of unbalanced design of
miacroarray experiments. In microarray experiments, experimental design is a crucial
starting-point for obtaining reasonable results. In a two-class problem, an equal or
similar number of samples it should be collected between the two classes. However in
some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose
to address this issue by applying a modified version of SAM [2]. MultiSAM consists in
a reiterated application of a SAM analysis, comparing the less populated class (LPC)
with 1,000 random samplings of the same size from the more populated class (MPC) A
list of the differentially expressed genes is generated for each SAM application. After
1,000 reiterations, each single probe given a "score"
ranging from 0 to 1,000 based on its
recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM
was compared to the performance of SAM and LIMMA [3] over two simulated data
sets via beta and exponential distribution. The results of all three algorithms over low-
noise data sets seems acceptable However, on a real unbalanced two-channel data set
reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds
23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical
clustering. We also report extra-assay validation in terms of differentially expressed
genes Although standard algorithms perform well over low-noise simulated data sets,
multi-SAM seems to be the only one able to reveal subtle differences in gene expression
profiles on real unbalanced data.
In Chapter 4 a method to adress similarities evaluation in a three-class prblem by
means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in
a prognostic and diagnostic clinical framework, not only differences could have a crucial
role. In some cases similarities can give useful and, sometimes even more, important
information. The goal, given three classes, could be to establish, with a certain level
of confidence, if the third one is similar to the first or the second one. In this work
we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the
limitation of standard supervised classification. In fact, RVM offers many advantages
compared, for example, with his well-known precursor (Support Vector Machine - SVM
[3]). Among these advantages, the estimate of posterior probability of class membership
represents a key feature to address the similarity issue. This is a highly important, but
often overlooked, option of any practical pattern recognition system. We focused on
Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of
grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate
G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for
samples of G2 to be member of class G1 or class G3. The analysis showed that breast
cancer samples of grade II have a molecular profile more similar to breast cancer samples
of grade I. Looking at the literature this result have been guessed, but no measure of
significance was gived before.
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Zhu, Yitan. "Learning Statistical and Geometric Models from Microarray Gene Expression Data." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28924.
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In this dissertation, we propose and develop innovative data modeling and analysis methods for extracting meaningful and specific information about disease mechanisms from microarray gene expression data.
To provide a high-level overview of gene expression data for easy and insightful understanding of data structure, we propose a novel statistical data clustering and visualization algorithm that is comprehensively effective for multiple clustering tasks and that overcomes some major limitations of existing clustering methods. The proposed clustering and visualization algorithm performs progressive, divisive hierarchical clustering and visualization, supported by hierarchical statistical modeling, supervised/unsupervised informative gene/feature selection, supervised/unsupervised data visualization, and user/prior knowledge guidance through human-data interactions, to discover cluster structure within complex, high-dimensional gene expression data.
For the purpose of selecting suitable clustering algorithm(s) for gene expression data analysis, we design an objective and reliable clustering evaluation scheme to assess the performance of clustering algorithms by comparing their sample clustering outcome to phenotype categories. Using the proposed evaluation scheme, we compared the performance of our newly developed clustering algorithm with those of several benchmark clustering methods, and demonstrated the superior and stable performance of the proposed clustering algorithm.
To identify the underlying active biological processes that jointly form the observed biological event, we propose a latent linear mixture model that quantitatively describes how the observed gene expressions are generated by a process of mixing the latent active biological processes. We prove a series of theorems to show the identifiability of the noise-free model. Based on relevant geometric concepts, convex analysis and optimization, gene clustering, and model stability analysis, we develop a robust blind source separation method that fits the model to the gene expression data and subsequently identify the underlying biological processes and their activity levels under different biological conditions.
Based on the experimental results obtained on cancer, muscle regeneration, and muscular dystrophy gene expression data, we believe that the research work presented in this dissertation not only contributes to the engineering research areas of machine learning and pattern recognition, but also provides novel and effective solutions to potentially solve many biomedical research problems, for improving the understanding about disease mechanisms.Ph. D.
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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/.
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Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the raw data to handle some extreme cases; the second improvement is modifying the strategy to generate better clusters. Simulation data and real microarray data were used to evaluate these improvements; this approach could efficiently generate more accurate clusters. A new feature-based algorithm was also developed in which steady state value; overshoot, rise time, settling time and peak time are generated by the 2nd order control system for the clustering purpose. This feature-based approach is much faster and more accurate than the existing profile-based algorithm for long time-series data.
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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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Wood, 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.
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Bosio, 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.
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In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and eficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet.
Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process.
This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk.
The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers.
In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions. All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art. Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.En 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.
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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.
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Hong, 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.
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Leng, 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.
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Fält, 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/.
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Lim, Sanghyun. "Sorghum gene expression modulated by water deficit and cold stress." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4705.
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Global gene expression in Sorghum bicolor, an important crop showing drought
tolerance in arid and semi-arid cultivated areas, was monitored to exposure of 8-days
seedlings to water deficit (20% polyethylene glycol) or cold stress (4 úC). A sorghum
cDNA microarray, including ~13,000 (milestone version 1) or ~28,000 (milestone
version 2) unigenes, was used to examine gene expression in shoots and roots at 3 and
27hours after stress treatment. ~1,300 and ~2,300 genes were modulated by water deficit
and cold stress, respectively. Up-regulated genes included previously identified stressinduced
genes such as early drought-induced gene, dehydrin, late embryogenesis
abundant gene, glycin and proline-rich gene, and water stress-inducible genes as well as
unknown genes. Genes involved in signal transduction, lipid metabolism, transporter,
and carbohydrate metabolism are induced. Quantitative real-time PCR was used to
quantify changes in relative mRNA abundance for 333 and 108 genes in response to
water deficit and cold stress, respectively. Stress-induced genes were classified by
kinetics. Eighteen of 108 cold-induced genes were modulated by cold but not by ABA and PEG treatment. This research provides the starting point for detailed analysis and
comparison of water deficit and cold modulated gene networks in sorghum.
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Lili, Loukia. "Computational analyses of gene expression profiles of ovarian and pancreatic cancer." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52911.
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Cancer is a devastating disease for human society with thousands of deaths and estimated new cases every year around the globe. Intensive research efforts on understanding the disease progression and determining effective diagnostics and therapeutics have been employed for over one hundred years. Throughout this time, and in particular during the last two decades, computational-based methods have gained increasing importance in cancer biology research by providing significant advantages in the analysis and interpretation of high-throughput data at the molecular and genomic levels.
More specifically, after completion of the Human Genome Project in 2003, and with the Cancer Human Genome Project underway, high-throughput biological assays (e.g., microarray chips, next generation sequencing machines) have supplied researchers thousands of measurements per experimental sample. The massive amount of related data has oftentimes been challenging to interpret and translate, particularly in cancer biology and therapeutics. This thesis reports the results of three independent studies in which high-throughput gene expression is computationally analyzed to address longstanding issues in cancer biology. Two of the studies utilize data from ovarian cancer patients while the third involves data collected from pancreatic cancer patients.
In Chapter 1, I address the importance of personalized profiling in pancreatic cancer ; in Chapter 2 the role of cancer stroma in the progression of ovarian cancer and in Chapter 3 evidence for the role of epithelial-to-mesenchymal transition (EMT) in ovarian cancer metastasis.
More specifically, Chapter 1 emphasizes the power of personalized molecular profiling in unmasking unique gene expression signatures that correspond to each individual patient. These individual expression patterns (individual profiling), which may be overlooked by the traditional methods of gene signatures enriched in groups of afflicted individuals (group profiling), can provide valuable information for more successful targeted therapies. In order to address this issue in pancreatic cancer, comparisons of the most significantly differentially expressed genes and functional pathways were performed between cancer and control patient samples as determined by group vs. personalized analyses. There was little to no overlap between genes/pathways identified by group analyses relative to those identified by personalized analyses. These results indicated that personalized and not group molecular profiling is the most appropriate approach for the identification of putative candidates for targeted gene therapy of pancreatic and perhaps other cancers with heterogeneous molecular etiology.
Chapter 2, also with strong implications on personalized molecular profiling, unveils the functional variability of the tumor microenvironment among ovarian cancer patients. The purpose of this study was to investigate the process of microenvironmental stroma activation in human ovarian cancer by molecular analysis of matched sets of cancer and surrounding stroma tissues from individual patients. Expression patterns of genes encoding signaling molecules and compatible receptors in the cancer stroma and cancer epithelia samples indicated the existence of two sub-groups of cancer stroma with different propensities to support tumor growth. These results demonstrated that functionally significant variability exists among ovarian cancer patients in the ability of the microenvironment to modulate cancer development.
Chapter 3 aims to uncover the molecular mechanisms that underlie the metastatic process with the hope that such knowledge may lead to more effective therapeutic treatments. For this purpose, pathological and molecular analyses were conducted in 14 matched sets of primary and metastatic samples from late staged ovarian cancer patients. Pathological examination revealed no morphological differences between any of the primary and metastatic samples. In contrast, gene expression analyses identified two distinct groups of patient samples. One group displayed essentially identical expression patterns to primary samples isolated from the same patients. The second group displayed expression patterns significantly different from primary samples isolated from the same patients. Predominant among the differentially expressed genes characterizing this second class of metastatic samples were genes previously associated with epithelial-to-mesenchymal transtion (EMT). These results supported a role of EMT in at least some ovarian cancer metastases and demonstrated that indistinguishable morphologies between primary and metastatic cancer samples is not sufficient evidence to negate the role of EMT in the metastatic process.The 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
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Gabbur, Prasad. "Machine Learning Methods for Microarray Data Analysis." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195829.
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Microarrays emerged in the 1990s as a consequence of the efforts to speed up the process of drug discovery. They revolutionized molecular biological research by enabling monitoring of thousands of genes together. Typical microarray experiments measure the expression levels of a large numberof genes on very few tissue samples. The resulting sparsity of data presents major challenges to statistical methods used to perform any kind of analysis on this data. This research posits that phenotypic classification and prediction serve as good objective functions for both optimization and evaluation of microarray data analysis methods. This is because classification measures whatis needed for diagnostics and provides quantitative performance measures such as leave-one-out (LOO) or held-out prediction accuracy and confidence. Under the classification framework, various microarray data normalization procedures are evaluated using a class label hypothesis testing framework and also employing Support Vector Machines (SVM) and linear discriminant based classifiers. A novel normalization technique based on minimizing the squared correlation coefficients between expression levels of gene pairs is proposed and evaluated along with the other methods. Our results suggest that most normalization methods helped classification on the datasets considered except the rank method, most likely due to its quantization effects.Another contribution of this research is in developing machine learning methods for incorporating an independent source of information, in the form of gene annotations, to analyze microarray data. Recently, genes of many organisms have been annotated with terms from a limited vocabulary called Gene Ontologies (GO), describing the genes' roles in various biological processes, molecular functions and their locations within the cell. Novel probabilistic generative models are proposed for clustering genes using both their expression levels and GO tags. These models are similar in essence to the ones used for multimodal data, such as images and words, with learning and inference done in a Bayesian framework. The multimodal generative models are used for phenotypic class prediction. More specifically, the problems of phenotype prediction for static gene expression data and state prediction for time-course data are emphasized. Using GO tags for organisms whose genes have been studied more comprehensively leads to an improvement in prediction. Our methods also have the potential to provide a way to assess the quality of available GO tags for the genes of various model organisms.
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Zhu, 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.
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Stelios, Pavlidis. "Pathway based microarray analysis based on multi-membership gene regulation." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6968.
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Recent developments in automation and novel experimental techniques have led to the accumulation of vast amounts of biological data and the emergence of numerous databases to store the wealth of information. Consequentially, bioinformatics have drawn considerable attention, accompanied by the development of a plethora of tools for the analysis of biological data. DNA microarrays constitute a prominent example of a high-throughput experimental technique that has required substantial contribution of bioinformatics tools. Following its popularity there is an on-going effort to integrate gene expression with other types of data in a common analytical approach. Pathway based microarray analysis seeks to facilitate microarray data in conjunction with biochemical pathway data and look for a coordinated change in the expression of genes constituting a pathway. However, it has been observed that genes in a pathway may show variable expression, with some appearing activated while others repressed. This thesis aims to add some contribution to pathway based microarray analysis and assist the interpretation of such observations, based on the fact that in all organisms a substantial number of genes take part in more than one biochemical pathway. It explores the hypothesis that the expression of such genes represents a net effect of their contribution to all their constituent pathways, applying statistical and data mining approaches. A heuristic search methodology is proposed to manipulate the pathway contribution of genes to follow underlying trends and interpret microarray results centred on pathway behaviour. The methodology is further refined to account for distinct genes encoding enzymes that catalyse the same reaction, and applied to modules, shorter chains of reactions forming sub-networks within pathways. Results based on various datasets are discussed, showing that the methodology is promising and may assist a biologist to decipher the biochemical state of an organism, in experiments where pathways exhibit variable expression.
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Hsu, Jessie. "Outcome-Driven Clustering of Microarray Data." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10410.
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The rapid technological development of high-throughput genomics has given rise to complex high-dimensional microarray datasets. One strategy for reducing the dimensionality of microarray experiments is to carry out a cluster analysis to find groups of genes with similar expression patterns. Though cluster analysis has been studied extensively, the clinical context in which the analysis is performed is usually considered separately if at all. However, allowing clinical outcomes to inform the clustering of microarray data has the potential to identify gene clusters that are more useful for describing the clinical course of disease. The aim of this dissertation is to utilize outcome information to drive the clustering of gene expression data. In Chapter 1, we propose a joint clustering model that assumes a relationship between gene clusters and a continuous patient outcome. Gene expression is modeled using cluster specific random effects such that genes in the same cluster are correlated. A linear combination of these random effects is then used to describe the continuous clinical outcome. We implement a Markov chain Monte Carlo algorithm to iteratively sample the unknown parameters and determine the cluster pattern. Chapter 2 extends this model to binary and failure time outcomes. Our strategy is to augment the data with a latent continuous representation of the outcome and specify that the risk of the event depends on the latent variable. Once the latent variable is sampled, we relate it to gene expression via cluster specific random effects and apply the methods developed in Chapter 1. The setting of clustering longitudinal microarrays using binary and survival outcomes is considered in Chapter 3. We propose a model that incorporates a random intercept and slope to describe the gene expression time trajectory. As before, a continuous latent variable that is linearly related to the random effects is introduced into the model and a Markov chain Monte Carlo algorithm is used for sampling. These methods are applied to microarray data from trauma patients in the Inflammation and Host Response to Injury research project. The resulting partitions are visualized using heat maps that depict the frequency with which genes cluster together.
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Khamesipour, Alireza. "IMPROVED GENE PAIR BIOMARKERS FOR MICROARRAY DATA CLASSIFICATION." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1573.
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The Top Scoring Pair (TSP) classifier, based on the notion of relative ranking reversals in the expressions of two marker genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. We introduce the AUC-based TSP classifier, which is based on the Area Under the ROC (Receiver Operating Characteristic) Curve. The AUCTSP classifier works according to the same principle as TSP but differs from the latter in that the probabilities that determine the top scoring pair are computed based on the relative rankings of the two marker genes across all subjects as opposed to for each individual subject. Although the classification is still done on an individual subject basis, the generalization that the AUC-based probabilities provide during training yield an overall better and more stable classifier. Through extensive simulation results and case studies involving classification in ovarian, leukemia, colon, and breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative pivot genes. The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across {\em all} subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair.\\ We have also proposed the use of the AUC test statistic in order to reduce the computational cost of the TSP in selecting the most informative pair of genes for diagnosing a specific disease. We have proven the efficacy of our proposed method through case studies in ovarian, colon, leukemia, breast and prostate cancers and diffuse large b-cell lymphoma in selecting informative genes. We have compared the selected pairs, computational cost and running time and classification performance of a subset of differentially expressed genes selected based on the AUC probability with the original TSP in the aforementioned datasets. The reduce sized TSP has proven to dramatically reduce the computational cost and time complexity of selecting the top scoring pair of genes in comparison to the original TSP in all of the case studies without degrading the performance of the classifier. Using the AUC probability, we were able to reduce the computational cost and CPU running time of the TSP by 79\% and 84\% respectively on average in the tested case studies. In addition, the use of the AUC probability prior to applying the TSP tends to avoid the selection of genes that are not expressed (``pivot'' genes) due to the imposed condition. We have demonstrated through LOOCV and 5-fold cross validation that the reduce sized TSP and TSP have shown to perform approximately the same in terms of classification accuracy for smaller threshold values. In conclusion, we suggest the use of the AUC test statistic in reducing the size of the dataset for the extensions of the TSP method, e.g. the k-TSP and TST, in order to make these methods feasible and cost effective.
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Hasan, 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.
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Kirk, 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.
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The advent of microarray technology, allowing measurement of gene expression levels for thousands of genes in parallel, has made possible experiments designed to investigate the genetic control of variation in gene expression level (described in the literature as ???genetical genomics??? or ???eQTL??? experiments). Published results from these studies, in yeast and in mice, show that genetic variation is an important factor in gene regulation, and furthermore that individual polymorphisms modify the expression level of many genes. The concern of this thesis is the bioinformatic analyses of the expression level and genotype data sets that are the raw material for these studies. In particular this thesis addresses the two issues of detection of artefactual effects, and maximizing the information that can be extracted from the data. It is shown that while a polymorphism affecting the expression of many genes may be readily detected, care must be taken to determine whether the detected effect is genuinely one of genetic control of expression level, rather than the effect of correlations in measured expression level not of genetic cause. A significance test is devised to distinguish between these cases. The detection of artefactual correlation is explored further in the reanalysis of the published data from a large yeast study. A critique is given of the permutation method used to ascribe genetic control as the cause of inter gene expression level correlation. The presence of some degree of artefactual correlation is shown, and novel methods are presented for identifying such artefacts. To extend the analyses that may be applied to eQTL data, an algorithm is presented for determining secondary eQTLs for gene expression level (as opposed to a single primary QTL), along with a significance test for the putative QTL found. The technique is demonstrated on a large public data set. In addition to the use for which they are intended, the data sets generated for eQTL studies provide opportunities for additional analyses. In this thesis a method is developed for calculating a genome wide map of meiotic recombination frequency from the genotype data for multiple segregant strains. The method is demonstrated on the published genotype data generated for a large yeast eQTL study.
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Howell, 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.
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Turro, 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.
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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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Yamaga, Yuichi. "Gene expression profile of Dclk1+ cells in intestinal tumors." Kyoto University, 2019. http://hdl.handle.net/2433/236595.
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Chang, Xiaoqing. "Bayesian Mixtures and Gene Expression Profiling with Missing Data." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1226090856.
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Wennmalm, Kristian. "Analytical strategies for identifying relevant phenotypes in microarray data /." Stockholm, 2007. http://diss.kib.ki.se/2007/978-91-7357-401-3/.
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Yuan, Lihui. "Quorum sensing regulated gene expression in Porphyromonas gingivalis." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0010043.
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Thesis (Ph.D.)--University of Florida, 2005.Typescript. Title from title page of source document. Document formatted into pages; contains 134 pages. Includes Vita. Includes bibliographical references.
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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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Nguyen, 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.
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Microarray data clustering represents a basic exploratory tool to find groups of genes exhibiting similar expression patterns or to detect relevant classes of molecular subtypes. Among a wide range of clustering approaches proposed and applied in the gene expression community to analyze microarray data, mixture model-based clustering has received much attention to its sound statistical framework and its flexibility in data modeling. However, clustering algorithms following the model-based framework suffer from two serious drawbacks. The first drawback is that the performance of these algorithms critically depends on the starting values for their iterative clustering procedures. Additionally, they are not capable of working directly with very high dimensional data sets in the sample clustering problem where the dimension of the data is up to hundreds or thousands. The thesis focuses on the two challenges and includes the following contributions: First, the thesis introduces the statistical model of our proposed normalized Expectation Maximization (EM) algorithm followed by its clustering performance analysis on a number of real microarray data sets. The normalized EM is stable even with random initializations for its EM iterative procedure. The stability of the normalized EM is demonstrated through its performance comparison with other related clustering algorithms. Furthermore, the normalized EM is the first mixture model-based clustering approach to be capable of working directly with very high dimensional microarray data sets in the sample clustering problem, where the number of genes is much larger than the number of samples. This advantage of the normalized EM is illustrated through the comparison with the unnormalized EM (The conventional EM algorithm for Gaussian mixture model-based clustering). Besides, for experimental microarray data sets with the availability of class labels of data points, an interesting property of the convergence speed of the normalized EM with respect to the radius of the hypersphere in its corresponding statistical model is uncovered. Second, to support the performance comparison of different clusterings a new internal index is derived using fundamental concepts from information theory. This index allows the comparison of clustering approaches in which the closeness between data points is evaluated by their cosine similarity. The method for deriving this internal index can be utilized to design other new indexes for comparing clustering approaches which employ a common similarity measure.
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Ball, 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.
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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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Falin, Lee J. "Systems Uncertainty in Systems Biology & Gene Function Prediction." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26634.
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The widespread use of high-throughput experimental assays designed to measure the entire complement of a cells genes or gene products has led to vast stores of data which are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. If the goal is to use this data to infer network models, these sparse datasets can lead to under-determined systems. While model parameter variation and its effects on model robustness has been well studied, most of this work has looked exclusively at accounting for variation only from measurement error. In contrast, little work has been done to isolate and quantify the amount of parameter variation caused by the uncertainty in the unmeasured regions of time course experiments.
Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured inter- vals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within un- measured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. We also present an application of this method to isolate and quantify two distinct sources of model parameter variation. In the concluding chapter we discuss another source of uncertainty in systems biology, namely gene function prediction, and compare several algorithms designed for that purpose.Ph. D.
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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.
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Todays 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
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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.
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Perera, 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/.
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QTL mapping with segregating populations results in poor map resolution which limits the applicability of mapped QTL in further research such as gene cloning. The current research project aimed mainly at developing STepped Aligned Recombinant Inbred Strains (STAIRS) covering the top region of chromosome 3 and demonstrating the feasibility of using STAIRS in high resolution mapping of QTL in Arabidopsis. The top region of chromosome 3 of Arabidopsis had been reported to house QTL related to flowering time. This region was first saturated with 24 polymorphic microsatellite markers and 23 narrow STAIRS were produced within the region via a marker-assisted backcross breeding programme using whole chromosome substitution lines. The analysis of QTL with the narrow STAIRS revealed a major pleiotropic QTL within 2-3 cM affecting flowering time, leaf number at day 20 and rosette and cauline leaf numbers at flowering. A second QTL with less but opposite effect on the same traits were located within 15-20 cM. The search for candidate genes within 2-3 cM of chromosome 3, to locate possible candidate genes revealed COL-2, CONSTANS-Like gene which affects flowering time. Microarray gene expression profiling was performed using the two genotypically closest lines which differ for flowering time to compare the two lines at the same chronological and physiological ages in two experiments respectively. The lists of differentially expressed genes were obtained from the two experiments. Differential expression was observed for the possible candidate gene in the latter experiment. The results emphasized the power of STAIRS in fine mapping of QTL and the possibility of using them in transcriptional profiling to study the expression of genes.
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Xiang, 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.
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Previous studies have demonstrated specific biochemical abnormalities in the noradrenergic locus coeruleus (LC) that are strongly associated with major depressive disorder (MDD). Here, we studied the LC of 4 pairs of MDD and matched control subjects by gene expression microarray analysis in an effort to accelerate the discovery of pathobiological abnormalities of these cells in MDD. Among matching criteria, pH values of control (6.71±0.06) and MDD (6.66±0.12) subjects were closely matched. Gene expression profiling using whole human genome microarrays (Agilent) revealed statistically significant changes in approximately 50 transcripts in the LC of depressive subjects. Quantitative real-time PCR (qPCR) was used to analyze transcripts identified by microarray anlayses. In initial studies of 11 of these transcripts that demonstrated a >2-fold change in microarrays, only 3 transcripts were confirmed by qQPCR in a larger sample of 11-12 pairs of MDD and matched control subjects. Amounts of bone morphogenetic factor-7 (BMP7; p=0.001) and potassium channel subfamily K, member 7 (KCNK7; p=0.049) mRNAs were significantly lower in MDD subjects compared to control subjects (~2-fold difference). In contrast, neurolysin mRNA levels were significantly higher (~3-fold; p=0.03) in MDD than in control subjects. BMP7 is a member of the TGF-β superfamily and has neuroprotective and neurotrophic effects on catecholaminergic neurons. The KCNK family of potassium channels contribute to the excitability of neurons. Neurolysin is a zinc-dependent metallopeptidase involved in neuropeptide metabolism. The present study is the first report of these novel gene expression abnormalities in the LC of MDD subjects. These findings enhance our understanding of the pathobiology of MDD and may represent novel targets for pharmacological management of depression.
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Shen, 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.
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