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1
Petronella, Nicholas. "Gene Conversions and Selection in the Gene Families of Primates." Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20538.
Testo completoAbstract (sommario):
We used the GENECONV program, the Hsu et al. (2010) method and phylogenetic analyses to analyze the gene conversions which occurred in the growth hormone, folate receptor and trypsin gene families of six primate species. Significant positive correlations were found between sequence similarity and conversion length in all but the trypsin gene family. Converted regions, when compared to non-converted ones, also displayed a significantly higher GC-content in the growth hormone and folate receptor gene families. Finally, all detected gene conversions were found to be less frequent in conserved gene regions and towards functionally important genes. This suggests that purifying selection is eliminating all gene conversions having a negative functional impact.
2
Zid, Mouldi. "Gene Conversions in the Siglec and CEA Immunoglobulin Gene Families of Primates." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23625.
Testo completoAbstract (sommario):
Siglecs and CEA are two families of cell surface proteins belonging to the immunoglobulin superfamily. They are thought to be involved in cell-cell interactions and have various other biological functions. We used the GENECONV program that applies statistical tests to detect gene conversion events in each family of five primate species. For the Siglec family, we found that gene conversions are frequent between CD33rSiglec genes, but are absent between their conserved Siglec genes. For the CEA family, half of gene conversion events detected are located in coding regions. A significant positive correlation was found between the length of the conversions and the similarity of the converted regions only in the Siglec gene family. Moreover, we found an increase in GC-content and similarity in converted regions compared to non-converted regions of the two families. Furthermore, in the two families, gene conversions occur more frequently in the extracellular domains of proteins, and rarely in their transmembrane and cytoplasmic regions. Finally, these two families appear to be evolving neutrally or under negative selection.
3
Liu, Zhilin. "Gene expression profiling of bovine ovarian follicular selection." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4490.
Testo completoAbstract (sommario):
Thesis (Ph.D.)--University of Missouri-Columbia, 2006.<br>The entire dissertation/thesis text is included in the research.pf file; the official abstract appears in the short.pf file (which also appears in the research.pf); a non-technical general description, or public abstract, appears in the public.pf file. Title from title screen of research.pf file (viewed on May 6, 2009) Vita. Includes bibliographical references.
4
Huisman, Jisca. "Gene Flow and Natural Selection in Atlantic Salmon." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for biologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-16991.
Testo completo
5
Chen, Li. "Ranking-Based Methods for Gene Selection in Microarray Data." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3888.
Testo completoAbstract (sommario):
DNA microarrays have been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. One of the major goals of microarray data analysis is the detection of differentially expressed genes across two kinds of tissue samples or samples obtained under two experimental conditions. A large number of gene detection methods have been developed and most of them are based on statistical analysis. However the statistical analysis methods have the limitations due to the small sample size and unknown distribution and error structure of microarray data. In this thesis, a study of ranking-based gene selection methods which have weak assumption about the data was done. Three approaches are proposed to integrate the individual ranks to select differentially expressed genes in microarray data. The experiments are implemented on the simulated and biological microarray data, and the results show that ranking-based methods outperform the t-test and SAM in selecting differentially expressed genes, especially when the sample size is small.
6
Perucchini, Matteo. "The cervid PrP gene : patterns of variability and selection." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/15634.
Testo completoAbstract (sommario):
Variation at codon 132 of the <i>Cervus canadensis </i>(wapiti) <i>PRNP</i> has been claimed to modulate Chronic Wasting Disease (CWD), a relatively new TSE affecting cervid species and currently the only TSE naturally affecting both captive and free-ranging populations. Codon 132 corresponds to the human codon 129 and variation at this position has been associated with TSE-related balancing selection in humans. This thesis investigated the genetic variability and selective patterns of coding and non-coding regions of <i>PRNP</i> in free-ranging populations of <i>C. Canadensis </i>and <i>C. elaphus </i>(CWD-free species closely related to wapiti) to gain a better understanding of the possible functional or disease-related forces shaping PrP genetics. The study of codon 132 genotype patterns in CWD<sub>+VE </sub>and CWD<sub>-VE</sub> wapiti provided no evidence for genetic modulation of CWD susceptibility, challenging previously published data. Despite this, a modulatory role of this residue in CWD incubation time, as suggested by many, is still possible. The analysis of the variability patterns in the PrP gene of the two cervid species suggested the presence of purifying selection. This was also supported by analyses aimed at identifying positively selected sites, which showed that codon 100 was the only site under positive selection throughout mammalian evolution, while the rest of the protein was under strong purifying selection. These data provide further support for the hypothesis suggesting a key cellular role for the PrP protein. The adaptive pressures driving selection at codon 100 are unknown, although they are most likely to be related to PrP function. A role for variation at this position in the interaction of the PrP protein with cell membrane translocation factors is proposed. The study provided an insight into the possible forces shaping PrP genetics and revaluated the role of variation at codon 132 in the wapiti <i>PRNP</i> gene in relation to CWD susceptibility.
7
Medeiros, Lucas Paoliello de. "Coevolution in mutualistic networks: gene flow and selection mosaics." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/41/41134/tde-17102017-154829/.
Testo completoAbstract (sommario):
Ecological interactions such as predation, competition, and mutualism are important forces that influence species evolution. Coevolution is defined as reciprocal evolutionary change in interacting species. The Geographic Mosaic Theory of Coevolution (GMTC) provides a theoretical framework to explain how collections of populations should coevolve across space. Two fundamental aspects of the GMTC are gene flow among populations and the presence of selection mosaics, which are collections of localities with particular selection regimes. Several studies have explored how phenotypic trait matching between species evolves in pairs or small groups of species. However, ecological interactions frequently form large networks that connect dozens of species present in a given community. In networks of mutualisms, for instance, the organization of interactions may affect ecological and evolutionary processes. A next step in understanding the coevolutionary process is to investigate how aspects of the GMTC affect the evolution of species embedded in interaction networks. In this dissertation, we tried to fill this gap using a mathematical model of coevolution, complex networks tools, and information on empirical mutualistic networks. Our numerical simulations of the coevolutionary model allow us to draw three main conclusions. First, gene flow affects trait patterns generated by coevolution and may favor the emergence of trait matching depending on the selection mosaic. Second, the organization of mutualistic networks influences coevolution, but this effect may vanish when gene flow favors trait matching. Intimate mutualisms, such as protection of host plants by ants, form small and compartmentalized networks that generate higher trait matching than large and nested networks typical of mutualisms among free-living species, such as pollination. Third, habitat fragmentation resulting in the disruption of gene flow should reduce the reciprocal adaptations between interacting species and at the same time promote adaptations to the local abiotic environment. In conclusion, we show that a complex interplay between gene flow, the geographic structure of selection, and the organization of ecological networks shapes the evolution of large groups of species. Our results therefore allow predictions of how environmental impacts such as habitat fragmentation will modify the evolution of species interactions<br>Interações ecológicas como predação, competição e mutualismo são importantes forças que influenciam a evolução de espécies. Chamamos de coevolução a mudança evolutiva recíproca em espécies que interagem. A Teoria do Mosaico Geográfico da Coevolução (TMGC) fornece um arcabouço teórico para entender como conjuntos de populações coevoluem ao longo do espaço. Dois aspectos fundamentais da TMGC são o fluxo gênico entre populações e a presença de mosaicos de seleção, isto é, conjuntos de locais com regimes de seleção particulares. Diversos estudos exploraram como o acoplamento entre fenótipos de diferentes espécies evolui em pares ou pequenos grupos de espécies. Entretanto, interações ecológicas frequentemente formam grandes redes que conectam dezenas de espécies presentes em uma comunidade. Em redes de mutualismos, por exemplo, a organização das interações pode influenciar processos ecológicos e evolutivos. Um próximo passo para a compreensão do processo coevolutivo consiste em investigar como aspectos da TMGC influenciam a evolução de espécies em redes de interações. Nesta dissertação, tentamos preencher esta lacuna usando um modelo matemático de coevolução, ferramentas de redes complexas e informação sobre redes mutualistas empíricas. Nossas simulações numéricas do modelo coevolutivo apontam para três principais conclusões. Primeiro, o fluxo gênico influencia os padrões fenotípicos gerados por coevolução e pode favorecer a emergência de acoplamento fenotípico entre espécies dependendo do mosaico de seleção. Segundo, a organização de redes mutualistas influencia a coevolução, mas este efeito pode desaparecer quando o fluxo gênico favorece acoplamento fenotípico. Mutualismos íntimos, como proteção de plantas hospedeiras por formigas, formam redes pequenas e compartimentalizadas que geram um maior acoplamento fenotípico do que as redes grandes e aninhadas típicas de mutualismos entre espécies de vida livre, como polinização. Por fim, a fragmentação de habitat, ao extinguir o fluxo gênico, pode reduzir as adaptações recíprocas entre espécies e ao mesmo tempo tornar cada espécie mais adaptada ao seu ambiente abiótico local. Em suma, mostramos que interações complexas entre fluxo gênico, estrutura geográfica da seleção e organização de redes ecológicas moldam a evolução de grandes grupos de espécies. Dessa forma, podemos traçar previsões sobre como impactos ambientais como a fragmentação de habitat irão alterar a evolução de interações ecológicas
8
Dai, Xiaotian. "Novel Statistical Models for Quantitative Shape-Gene Association Selection." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6856.
Testo completoAbstract (sommario):
Other research reported that genetic mechanism plays a major role in the development process of biological shapes. The primary goal of this dissertation is to develop novel statistical models to investigate the quantitative relationships between biological shapes and genetic variants. However, these problems can be extremely challenging to traditional statistical models for a number of reasons: 1) the biological phenotypes cannot be effectively represented by single-valued traits, while traditional regression only handles one dependent variable; 2) in real-life genetic data, the number of candidate genes to be investigated is extremely large, and the signal-to-noise ratio of candidate genes is expected to be very high. In order to address these challenges, we propose three statistical models to handle multivariate, functional, and multilevel functional phenotypes, with applications to biological shape data using different shape descriptors. To the best of our knowledge, there is no statistical model developed for multilevel functional phenotypes. Even though multivariate regressions have been well-explored and these approaches can be applied to genetic studies, we show that the model proposed in this dissertation can outperform other alternatives regarding variable selection and prediction through simulation examples and real data examples. Although motivated ultimately by genetic research, the proposed models can be used as general-purpose machine learning algorithms with far-reaching applications.
9
Riddoch, B. "Selection component analysis of the PGI polymorphism in Sphaeroma rugicauda." Thesis, University of Essex, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378440.
Testo completo
10
Panji, Sumir. "Identification of bacterial pathogenic gene classes subject to diversifying selection." Thesis, University of the Western Cape, 2009. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_5842_1297942831.
Testo completoAbstract (sommario):
<p>Availability of genome sequences for numerous bacterial species comprising of different bacterial strains allows elucidation of species and strain specific adaptations that facilitate their survival in widely fluctuating micro-environments and enhance their pathogenic potential. Different bacterial species use different strategies in their pathogenesis and the pathogenic potential of a bacterial species is dependent on its genomic complement of virulence factors. A bacterial virulence factor, within the context of this study, is defined as any endogenous protein product encoded by a gene that aids in the adhesion, invasion, colonization, persistence and pathogenesis of a bacterium within a host. Anecdotal evidence suggests that bacterial virulence genes are undergoing diversifying evolution to counteract the rapid adaptability of its host&rsquo<br>s immune defences. Genome sequences of pathogenic bacterial species and strains provide unique opportunities to study the action of diversifying selection operating on different classes of bacterial genes.</p>
11
Wood, S. Morwenna. "Oxygen sensing and gene expression : selection and analysis of mutant cells." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297427.
Testo completo
12
Nasiadka, Andrzej. "Gene-regulatory interactions and mechanisms of target gene selection of the Drosophila homeodomain protein Fushi tarazu." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ63722.pdf.
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13
Liu, Yushi. "Properties of the SCOOP Method of Selecting Gene Sets." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1280163360.
Testo completo
14
Berggren, Bremdal Karin. "Evolution of MHC Genes and MHC Gene Expression." Doctoral thesis, Uppsala universitet, Evolutionsbiologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122011.
Testo completoAbstract (sommario):
Polymorphism in coding regions and regions controlling gene expression is the major determinant of adaptive differences in natural populations. Genes of the major histocompatibility complex (MHC) possess a high level of genetic variation, which is maintained by selection over long coalescence times. MHC genes encode antigen-presenting molecules in the adaptive immune system, which protects the host from infectious diseases. However, MHC molecules may also present self-peptides and for most autoimmune diseases there is a genetic factor associated with the MHC. MHC genes have been used to learn about the interplay of selection and historical population events. In domestic dogs and their progenitor, the wolf, I explored factors associated with domestication and breed formation and their influence not only on MHC coding regions but also on the haplotypic structure of the class II region. Polymorphism and strong selection was demonstrated in the proximal promoters of MHC genes in dogs and wolves. Hence, genetic variation associated with MHC gene expression may have at least equal importance for a well functioning immune system. Associations between promoter sequences and particular coding alleles suggested allele-specific expression patterns. SNP haplotypes of the MHC class II region revealed ancestral as well as convergent haplotypes, in which combinations of alleles are kept by selection. Interestingly, weaker allelic associations were found between different genes and between coding regions and promoters in dogs compared to wolves. Potentially, this could cause insufficient defense against infections and predispose dogs to autoimmune diseases. For example, I identified a site in the promoter region that showed a consistent difference between haplotypes conferring susceptibility and protection to diabetes in dogs, which should be investigated further. Furthermore, I investigated how selection and demographic changes associated with glacial and inter-glacial periods have affected MHC variation in European hedgehogs and extended the prevailing knowledge concerning their population history.
15
Dong, Chunrong. "A housekeeping gene based procedure for the selection of differentially expressed genes for Affymetrix microarray experiments." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1278088559.
Testo completo
16
Assareh, Amin. "OPTIMIZING DECISION TREE ENSEMBLES FOR GENE-GENE INTERACTION DETECTION." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353971575.
Testo completo
17
Freudenberg, Johannes M. "Bayesian Infinite Mixture Models for Gene Clustering and Simultaneous Context Selection Using High-Throughput Gene Expression Data." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258660232.
Testo completo
18
Muttalib, Shahin. "The balance between selection and gene flow evaluated in threespine stickleback." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110425.
Testo completoAbstract (sommario):
Many populations seem adapted to their environment, through a match between their phenotype and habitat or resources. However, significant maladaptation is widespread, perhaps partly due to gene flow between populations, which prevents tracking a local optimum, by introducing alleles adapted to alternative conditions. Threespine stickleback show significant adaptive divergence over a variety of phenotypic traits, and the divergent lake-stream pairs of Vancouver Island illustrate the balance between selection and gene flow that exists in natural populations. In my thesis, I use the threespine stickleback stream populations of the Misty system, where the gene flow constraint in the outlet stream is high, to explore maladaptation. To determine whether gene flow is reducing adaptive divergence, I use measurement of selection to detect the gene flow constraint. My primary hypothesis is that selection should be higher in the outlet due to a higher deviation from the optimal stream phenotype. Using an individual tagging mark-recapture experiment over two years, I estimate natural selection on body shape, using several metrics of viability selection. I estimate total intensity of selection, and examine specific traits, such as body depth, that show important differences in divergence between the two sites. Using multivariate distances, I estimate both multivariate selection intensity and determine the direction of selection with reference to the inlet population. Results show inconsistent selection on body depth and overall body shape, suggesting that the pattern of selection is temporally dynamic, changing from one selection model to another. Different selection models may apply if the causality between divergence and gene flow is decoupled in each site. My project gives indications that to better answer the question of the role of gene flow in adaptation, it is necessary to quantify consequences for populations at both the trait level and the fitness level. Future attemps would also benefit from examining the role of sexual dimorphism, as well as using a broader suite of traits and fitness components.<br>Les populations naturelles sont généralement adaptées à leur environnement, et il existe une correspondance entre la morphologie et l'habitat ou les ressources. Pourtant, la maladaptation significative et elle aussi présente. Elle peut-être causé par flux génétique entre populations. Ce flux génétique, empêche la population d'atteindre leur optimum local, dû fait de l'introduction d'allèles adaptés aux conditions alternatives. Dans ma thèse, pour investiguer la maladaptation , j'utilise des populations d'épinoche à trois épines du système Misty, où la contrainte causée par le flux génétique est élevée. Pour séparer la causalité entre le flux génétique et la divergence adaptative, j'utilise la méthode de mesurer la sélection naturelle pour estimer la contrainte du flux génétique. L'épinoche à trois épines montre une divergence adaptative importante dans une variété de traits phénotypiques. Les paires lac-rivière de l'île de Vancouver illustrent bien l'équilibre entre la sélection et le flux génétique qu'il existe entre les populations des lacs et des rivières. En effectuant une expérience de capture marquage recapture individuelle sur deux ans, j'ai pu estimer la sélection naturelle sur la forme des poissons, en utilisant plusieurs mesures de sélection. J'ai estimé l'intensité totale de la sélection et ses effets sur des traits spécifiques, comme la profondeur du corps, qui montrent une divergence entre les deux sites. En utilisant des distances multivariées, j'ai estimé l'intensité de sélection multivariée et j'ai déterminé la direction de sélection en référence a la population du inlet. Mon hypothèse principale est que la sélection devrait être plus élevée dans le outlet du fait de son plus grande déviation du phénotype riverain typique. Les résultats indiquent une sélection variable sur la profondeur du corps, et sur la forme générale; ce qui suggère que le patron de sélection a une dynamique temporelle, changeant d'un modèle de sélection à un autre dans le temps. Les différents modèles de sélection ne s'appliqueront pas, si la causalité entre la divergence et le flux génétique est renversé dans les deux sites. Mes résultats indiquent que pour mieux répondre à la question du rôle du flux génétique dans l'adaptation, il est nécessaire de quantifier les conséquences pour les populations au niveau des traits et du fitness. Les travaux futurs intégreront aussi le rôle du dimorphisme sexuel en utilisant aussi une plus large gamme de traits et de composants de fitness.
19
Song, Youqiang. "Development of polymorphic molecular markers for bovine gene mapping and selection." Thesis, University of Reading, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262235.
Testo completo
20
Osborne, Owen Gregory. "Genomic analyses of gene flow and selection during diversification in Senecio." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:ffe5fb97-f0d0-4f6f-aed0-8cbb3226c1e5.
Testo completoAbstract (sommario):
Diversifying selection and gene flow were traditionally viewed as antagonistic forces, with diversifying selection promoting adaptation and speciation, and gene flow opposing them. However, their roles are far more complex than this. While gene flow can prevent speciation or initiate despeciation, it can also generate new hybrid species. Similarly, while adaptive divergence can be wiped out by gene flow, new adaptive variation can be introduced via hybridisation. The relative frequency of these outcomes, and indeed the frequency of gene flow and diversifying selection in general are largely unknown. This thesis illuminates these questions through evolutionary genomic analyses focussed on a recently diverged group of ragworts (Senecio). The Mediterranean Senecio species-complex contains several cases of hybrid speciation, as well as two species, S. aethnensis and S. chrysanthemifolius, which are a potential case of ecological speciation with gene flow, having adapted to high and low altitude habitats respectively on Mount Etna. However, their demography was previously un-studied. I first show that S. aethnensis and S. chrysanthemifolius diverged recently, at a time that coincides with the growth of Mount Etna to the altitudes which separate the species today, have experienced significant gene flow following their split, and are likely to be sister species, bolstering the hypothesis of ecological speciation. I further demonstrate that gene flow is common in the wider clade, and pinpoint multiple cases of gene flow amongst them. Finally, I identify several genes under positive selection in the clade, and show that the proportion of genes under selection is high relative to many other plant genera. The results further establish Senecio species as an invaluable model system for the study of diversification with gene flow, and suggest that high levels of gene flow and selection are features of their evolution, a situation which may prove to be common in plants.
21
Canul, Reich Juana. "An Iterative Feature Perturbation Method for Gene Selection from Microarray Data." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1588.
Testo completoAbstract (sommario):
Gene expression microarray datasets often consist of a limited number of samples relative to a large number of expression measurements, usually on the order of thousands of genes. These characteristics pose a challenge to any classification model as they might negatively impact its prediction accuracy. Therefore, dimensionality reduction is a core process prior to any classification task.
This dissertation introduces the iterative feature perturbation method (IFP), an embedded gene selector that iteratively discards non-relevant features. IFP considers relevant features as those which after perturbation with noise cause a change in the predictive accuracy of the classification model. Non-relevant features do not cause any change in the predictive accuracy in such a situation.
We apply IFP to 4 cancer microarray datasets: colon cancer (cancer vs. normal), leukemia (subtype classification), Moffitt colon cancer (prognosis predictor) and lung cancer (prognosis predictor). We compare results obtained by IFP to those of SVM-RFE and the t-test using a linear support vector machine as the classifier in all cases. We do so using the original entire set of features in the datasets, and using a preselected set of 200 features (based on p values) from each dataset. When using the entire set of features, the IFP approach results in comparable accuracy (and higher at some points) with respect to SVM-RFE on 3 of the 4 datasets. The simple t-test feature ranking typically produces classifiers with the highest accuracy across the 4 datasets. When using 200 features chosen by the t-test, the accuracy results show up to 3% performance improvement for both IFP and SVM-RFE across the 4 datasets. We corroborate these results with an AUC analysis and a statistical analysis using the Friedman/Holm test.
Similar to the application of the t-test, we used the methodsinformation gain and reliefF as filters and compared all three. Results of the AUC analysis show that IFP and SVM-RFE obtain the highest AUC value when applied on the t-test-filtered datasets. This result is additionally corroborated with statistical analysis.
The percentage of overlap between the gene sets selected by any two methods across the four datasets indicates that different sets of genes can and do result in similar accuracies.
We created ensembles of classifiers using the bagging technique with IFP, SVM-RFE and the t-test, and showed that their performance can be at least equivalent to those of the non-bagging cases, as well as better in some cases.
22
Lewis, Samuel Howard. "Evolution of Dipteran Argonaute genes through duplication, selection and functional specialisation." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/19569.
Testo completoAbstract (sommario):
The RNA interference (RNAi) mechanism is a conserved system of nucleic acid manipulation, based on the interaction between small RNA guide molecules and Argonaute effector proteins. RNAi pathways are found in the vast majority of eukaryotes, and have diversified into a broad array of functions including gene regulation, antiviral immunity and transposable element (TE) suppression. Many of these functional innovations coincide with duplication of Argonaute genes, suggesting that gene duplication may be a key driving force in the diversification of RNAi. However, few studies have explicitly investigated Argonaute evolution after duplication. In this thesis, I focused on the impact of gene duplication on the evolution of Argonaute genes. Argonaute genes in different species exhibit a broad array of functions; however, most of our knowledge of Argonaute function in the arthropods is based on studies in D. melanogaster. To compare the rate of duplication and its evolutionary effect between different Argonaute subclades, I quantified gene turnover rates and evolutionary rate change in Argonaute genes from 86 Dipteran species (Chapter 2). I find that duplication rate varies widely between subclades and lineages, and that duplication drives an increase in evolutionary rate, suggesting that functional divergence after Argonaute duplication is prevalent throughout the Diptera. In the obscura group of Drosophila I identified a series of recent duplications of Argonaute2 (Ago2), which has antiviral and anti-TE functions in D. melanogaster. To quantify the extent of functional divergence between these paralogues, I measured the expression of paralogues from three species (D. subobscura, D. obscura and D. pseudoobscura), in different tissues and under viral challenge (Chapter 3). I find that the majority of Ago2 paralogues have specialised to a derived testis-specific role, potentially to suppress TE activity or meiotic drive. While CRISPR-Cas9 mediated knockout of these genes ultimately proved unsuccessful (Chapter 5), the selective importance of their derived function is suggested by its multiple independent origins. Functional novelty, as appears to have evolved in the obscura group Ago2 paralogues, is often driven by strong selection. To quantify the evolutionary rate and positive selection on these paralogues, I gathered intraspecies polymorphism data for all paralogues in D. subobscura, D. obscura and D. pseudoobscura, combining this with publicly-available population genomic data for D. pseudoobscura (Chapter 4). I find that the majority of paralogues in all species have extremely low diversity, indicative of recent selection, and identify recent selective sweeps on three paralogues in D. pseudoobscura. This suggests that the majority of Ago2 paralogues in the obscura group are evolving under strong positive selection. In this thesis I have aimed to quantify the effect of gene duplication on Argonaute evolution. I find that Argonaute genes duplicate frequently in some lineages, resulting in the evolution of derived functions that may be driven by positive selection. This suggests that functional diversification is prevalent in eukaryotic RNAi, and is likely to coincide with expansion of the Argonaute gene family.
23
Xu, Yaomin. "New Clustering and Feature Selection Procedures with Applications to Gene Microarray Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1196144281.
Testo completo
24
Errington, Stephen J. "Target selection for antisense oligonucleotide induced exon skipping in the dystrophin gene." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2004. https://ro.ecu.edu.au/theses/794.
Testo completoAbstract (sommario):
Duchene Muscular Dystrophy (DMD), and the milder allelic Becker muscular dystrophy (EMD), are X-linked recessive muscle wasting disorders characterised by mutations in the dystrophin gene. DMD occurs at a frequency of approximately 1 in 3500 male newborns and life expectancy is typically less than 30 years. Due to progressive muscle wasting, affected boys are restricted to a wheelchair by the age of 12 years. The most common cause of death is pneumonia, compounded by cardiac involvement. Mutations that disrupt the dystrophin reading frame, or prevent the synthesis of either terminus, preclude the synthesis of a fully functional dystrophin. The subsequent Joss of membrane integrity results in a severe DMD phenotype, as these weakened but still functional muscle fibres are subject to continuous cycles of damage and regeneration. Conversely, BMD mutations are generally found to be in-frame deletions where a shorter but semi-functional protein with intact amino and carboxyl tennini has been synthesised. BMD has an incidence almost ten times less than DMD. While the distribution of myopathy parallels DMD, the onset and progression of BMD is variable and generally less severe and some affected individuals are virtually asymptomatic.
25
Makrinou, Eleni. "A cDNA selection approach to isolate Y-linked genes expressed in testis." Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326172.
Testo completo
26
Venegas-Ortiz, Juan. "Statistical mechanics of gene competition." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/9372.
Testo completoAbstract (sommario):
Statistical mechanics has been applied to a wide range of systems in physics, biology, medicine and even anthropology. This theory has been recently used to model the complex biochemical processes of gene expression and regulation. In particular, genetic networks offer a large number of interesting phenomena, such as multistability and oscillatory behaviour, that can be modelled with statistical mechanics tools. In the first part of this thesis we introduce gene regulation, genetic switches, and the colonization of a spatially structured media. We also introduce statistical mechanics and some of its useful tools, such as the master equation and mean- field theories. We present simple examples that are both pedagogical and also set the basis for the study of more complicated scenarios. In the second part we consider the exclusive genetic switch, a fundamental example of genetic networks. In this system, two proteins compete to regulate each other's dynamics. We characterize the switch by solving the stationary state in different limits of the protein binding and unbinding rates. We perform a study of the bistability of the system by examining its probability distribution, and by applying information theory techniques. We then present several versions of a mean field theory that offers further information about the switch. Finally, we compute the stationary probability distribution with an exact perturbative approach in the unbinding parameter, obtaining a valid result for a wide range of parameters values. The techniques used for this calculation are successfully applied to other switches. The topic studied in the third part of the thesis is the propagation of a trait inside an expanding population. This trait may represent resistance to an antibiotic or being infected with a certain virus. Although our model accounts for different examples in the genetic context, it is also very useful for the general study of a trait propagating in a population. We compute the speed of expansion and the stationary population densities for the invasion of an established and an expanding population, finding non-trivial criteria for speed selection and interesting speed transitions. The obtained formulae for the different wave speeds show excellent agreement with the results provided by simulations. Moreover, we are able to obtain the value of the speeds through a detailed analysis of the populations, and establish the requirements for our equations to present speed transitions. We finally apply our model to the propagation in a position-dependent fitness landscape. In this situation, the growth rate or the maximum concentration depends on the position. The amplitudes and speeds of the waves are again successfully predicted in every case.
27
Akhimienmhonan, Douglas. "An economic analysis of gene marker assisted seedstock selection in beef cattle." Thesis, University of British Columbia, 2006. http://hdl.handle.net/2429/96.
Testo completoAbstract (sommario):
This study analyzes the economic impact of a recent gene marker innovation for seedstock selection in beef cattle. Gene markers are being developed for many beef cattle attributes; this study focused on the tenderness quality of beef using two categories: tender and tough. The study begins by describing conventional procedures for seedstock selection, the science which underlies selection by gene markers and other non-genetic procedures currently being used to improve beef tenderness. After describing the commercialization of the gene marker innovation, a stylized model of a beef supply chain is constructed. The supply chain consists of a representative consumer, a producer/processor group and a monopolist supplier of the patented technology. Welfare changes resulting from the adoption of the innovation were simulated using four sets of demand elasticity data from literatures.
An important focus of this research is determining how the economic surplus from the innovation will be shared by consumers, producers and the gene marker monopolist. The consumer and gene marker monopolist benefit from the technology unless the marginal and fixed cost variables (not estimated in this study) of the monopolist, are excessively high. Producer surplus was simulated as positive with three of the four elasticity data sets. The share of surplus capture by producers is generally low relative to the gains captured by consumers and the gene marker monopolist. Comparative static analysis reveal that the benefit from the innovation varies across breeds, being higher for breeds in which the favorable form of the marker gene is more likely to be present.
Despite the apparent benefits of the innovation for beef supply chain participants, reported interviews with industry scientists reveal that markers should not be viewed as a replacement for conventional selection techniques. Indeed, selecting seedstock on the basis of a small number of available markers is not likely to produce the benefits that are currently being promised by life science companies. Consequently, this study recommends that the innovation be incorporated into existing seedstock selection practices. Much more analysis is needed to understand the full economic impact of gene markers for beef tenderness and for other beef quality attributes.
28
Coop, Graham M. "The likelihood of gene trees under selective models." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:ba97d36c-61c1-40c8-a1f4-e7ddc8918d5b.
Testo completoAbstract (sommario):
The extent to which natural selection shapes diversity within populations is a key question for population genetics. Thus, there is considerable interest in quantifying the strength of selection. In this thesis a full likelihood approach for inference about selection at a single site within an otherwise neutral fully-linked sequence of sites is developed. Integral to many of the ideas introduced in this thesis is the reversibility of the diffusion process, and some past approaches to this concept are reviewed. A coalescent model of evolution is used to model the ancestry of a sample of DNA sequences which have the selected site segregating. A novel method for simulating the coalescent with selection, acting at a single biallelic site, is described. Selection is incorporated through modelling the frequency of the selected and neutral allelic classes stochastically back in time. The ancestry is then simulated using a subdivided population model considering the population frequencies through time as variable population sizes. The approach is general and can be used for any selection scheme at a biallelic locus. The mutation model, for the selected and neutral sites, is the infinitely-many-sites model where there is no back or parallel mutation at sites. This allows a unique perfect phylogeny, a gene tree, to be constructed from the configuration of mutations on the sample sequences. An importance sampling algorithm is described to explore over coalescent tree space consistent with this gene tree. The method is used to assess the evidence for selection in a number of data sets. These are as follows: a partial selective sweep in the G6PD gene (Verrelli et al., 2002); a recent full sweep in the Factor IX gene (Harris and Hey, 2001); and balancing selection in the DCP1 gene (Rieder et al., 1999). Little evidence of the action of selection is found in the data set of Verrelli et al. (2002) and the data set of Rieder et al. (1999) seems inconsistent with the model of balancing selection. The patterns of diversity in the data set of Harris and Hey (2001) offer support of the hypothesis of a full sweep.
29
Steiger, Edgar [Verfasser]. "Efficient Sparse-Group Bayesian Feature Selection for Gene Network Reconstruction / Edgar Steiger." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1170876633/34.
Testo completo
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Fernando, Himesh. "Selection of G-quadruplex specific proteins and their effects on gene expression." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598998.
Testo completoAbstract (sommario):
G-quadruplexes in the promoters of oncogenes have been identified as potential therapeutic targets for the regulation of gene transcription by quadruplex-binding molecules. KIT is a tyrosine kinase receptor found on the cell membrane, and forms part of the cell signalling pathway. Certain mutations within it result in constitutive activation leading to uncontrolled cell proliferation. Elevated levels of KIT have been reported in most gastrointestinal cancer cases, hence making it a prime anti cancer target. <i>c-kit,</i> the gene encoding KIT, contains a G-rich sequence in its promoter. With the aid of circular dichroism, ultraviolet and <sup>1</sup>H NMR spectroscopic techniques, I have demonstrated that this G-rich sequence can form a G-quadruplex. An important criterion for any potential therapeutic is to be able to act specifically on the target of interest with minimum off target effects. Therefore, I screened for single-chain antibodies and zinc fingers from phage display libraries, that can bind to the <i>c-kit </i>promoter quadruplex with high affinity and specificity. I have demonstrated that engineered proteins can not only distinguish quadruplex from duplex DNA, but also can discriminate between two intramolecular quadruplexes with similar sequences, a molecular recognition achievement that had not been demonstrated before. Finally, the selected quadruplex-binding proteins were introduced into cancer cells to investigate if they had an effect on the transcription of <i>c-kit. </i>Furthermore, using human whole genome microarrays, I have asked the question ‘do engineered quadruplex binding proteins cause differential expression of genes that contain putative quadruplex forming sequences?’
31
McDonald, Kenneth W. "Gene Expression and Phenotype Response of Drosophila melanogaster to Selection." Digital Commons @ East Tennessee State University, 2008. https://dc.etsu.edu/etd/1967.
Testo completoAbstract (sommario):
The evolution of phenotypic plasticity is currently a topic of paramount interest in a diverse field of sub-disciplines. Salience is placed by all fields in describing the interaction of selection and phenotypic plasticity and the consequence of this interaction more broadly on evolution. Lacking in the discussion is substantial empirical description of genotype/phenotype interactions that by definition constitute the plastic response to novel and stressful environments. Here, I present empirical observations that bring the interaction of genotype and phenotype into focus. Drosophila melanogaster populations subjected to selection for tolerance to low food or high alcohol conditions each exhibited an enhancement of adaptive plasticity consistent with predictions associated broadly with the Baldwin Effect. Furthermore, each appears to have followed different courses of regulatory modification to achieve these ends. Broadly implicit in the results is the observation that previous exposure of the population to the conditions of induction may dictate the course of subsequent evolution of the phenotype.
32
McCluskey, Braedan. "Genome Evolution and Gene Expression Divergence in the Genus Danio." Thesis, University of Oregon, 2016. http://hdl.handle.net/1794/20484.
Testo completoAbstract (sommario):
Genus Danio includes zebrafish (Danio rerio) and several other phenotypically diverse species. To understand the history of these species and how they acquired the genetic differences underlying their diverse phenotypes, I performed two phylogenomic studies using Restriction-Site Associated DNA Sequencing and DNA hybridization-based exome enrichment. The results of these studies highlight important methodological considerations applicable to future experiments across taxa. Furthermore, these studies provide detailed understanding of the relationships within Danio including extensive introgression between lineages. The extent of introgression varies across the genome with regions of high recombination at the ends of chromosomes having the most evidence for introgression. Together, this work gives vital insight into the history of a model organism and the evolutionary processes that give rise to phenotypic diversity.
33
Chen, Xiaohui. "Comparisons of statistical modeling for constructing gene regulatory networks." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/4068.
Testo completoAbstract (sommario):
Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput
measurement devices are available, such as microarrays and
sequencing techniques, regulatory networks have been intensively studied
over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing
regression models and apply them to construct regulatory networks which
span trancription factors and microRNAs. We also propose an extended
algorithm to address the local optimum issue in finding the Maximum A
Posterjorj estimator. An E. coli mRNA expression microarray data set with
known bona fide interactions is used to evaluate our models and we show
that our regression networks with a properly chosen prior can perform comparably
to the state-of-the-art regulatory network construction algorithm.
Finally, we apply our models on a p53-related data set, NCI-60 data. By
further incorporating available prior structural information from sequencing
data, we identify several significantly enriched interactions with cell proliferation
function. In both of the two data sets, we select specific examples
to show that many regulatory interactions can be confirmed by previous
studies or functional enrichment analysis. Through comparing statistical
models, we conclude from the project that combining different models with
over-representation analysis and prior structural information can improve
the quality of prediction and facilitate biological interpretation.
Keywords: regulatory network, variable selection, penalized maximum
likelihood estimation, optimization, functional enrichment analysis.
34
Yu, Guoqiang. "Machine Learning to Interrogate High-throughput Genomic Data: Theory and Applications." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/28980.
Testo completoAbstract (sommario):
The missing heritability in genome-wide association studies (GWAS) is an intriguing open scientific problem which has attracted great recent interest. The interaction effects among risk factors, both genetic and environmental, are hypothesized to be one of the main missing heritability sources. Moreover, detection of multilocus interaction effect may also have great implications for revealing disease/biological mechanisms, for accurate risk prediction, personalized clinical management, and targeted drug design. However, current analysis of GWAS largely ignores interaction effects, partly due to the lack of tools that meet the statistical and computational challenges posed by taking into account interaction effects. Here, we propose a novel statistically-based framework (Significant Conditional Association) for systematically exploring, assessing significance, and detecting interaction effect. Further, our SCA work has also revealed new theoretical results and insights on interaction detection, as well as theoretical performance bounds. Using in silico data, we show that the new approach has detection power significantly better than that of peer methods, while controlling the running time within a permissible range. More importantly, we applied our methods on several real data sets, confirming well-validated interactions with more convincing evidence (generating smaller p-values and requiring fewer samples) than those obtained through conventional methods, eliminating inconsistent results in the original reports, and observing novel discoveries that are otherwise undetectable. The proposed methods provide a useful tool to mine new knowledge from existing GWAS and generate new hypotheses for further research.
Microarray gene expression studies provide new opportunities for the molecular characterization of heterogeneous diseases. Multiclass gene selection is an imperative task for identifying phenotype-associated mechanistic genes and achieving accurate diagnostic classification. Most existing multiclass gene selection methods heavily rely on the direct extension of two-class gene selection methods. However, simple extensions of binary discriminant analysis to multiclass gene selection are suboptimal and not well-matched to the unique characteristics of the multi-category classification problem. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone phenotypic up-regulated genes (OVEPUGs) and matches this gene selection with a one-versus-rest support vector machine. Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes, and intends to assure the statistical reproducibility and biological plausibility of the selected genes. We evaluated the fold changes of OVEPUGs and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed OVEPUG method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods.<br>Ph. D.
35
Romano, Eduardo O. "Selection indices for combining marker genetic data and animal model information /." This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-09192009-040546/.
Testo completo
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Shu, Cindy Chia-Fan Biotechnology & Biomolecular Sciences Faculty of Science UNSW. "Selection and isolation of high producing mammalian clones." Awarded by:University of New South Wales, 2007. http://handle.unsw.edu.au/1959.4/37031.
Testo completoAbstract (sommario):
This research studied recombinant DNA-derived protein expression utilising expression vectors containing IRES sequences to link the gene of interest with the gene encoding selectable marker in mammalian cell cultures. Polycistronic expression constructs utilising internal ribosome entry site (IRES) can link unrelated genes under control of a single promoter. Transient study on the IRESlinked gene expression was performed. It was possible to standardise the level of protein expression to plasmid number by determining the number of free plasmids in the cytoplasm. The expression of a selectable marker when downstream of IRES was reduced in comparison to the monocistronic construct. Importantly when IRES was used, there were no negative effects on recombinant gene expression upstream of IRES. Down-regulating the selectable marker gene expression has been shown to enhance the probability of obtaining highly expressing clones. To investigate the effects of down-regulating fusion metallothionein green fluorescent protein (MTGFP), new constructs were created to combine metal inducible M2.6 promoter to drive the expression of human growth hormone linked to MTGFP by an attenuated IRES. This resulted in less MTGFP expression, reduced survivability and mean fluorescence in the presence of heavy metal. The increased metal sensitivity lengthened the initial selection period using reduced metal concentration in comparison to cells transfected with wildtype MTGFP. FACS can be used to select for resistance conferred by MTGFP despite reduced expression. FACS enrichment and sorting increased the hGH expression, which was correlated with mean fluorescence of the population; therefore fluorescence can be used as an indication of the final recombinant protein expression. Different approaches to isolate suitable clones were also investigated. It is preferable to select the transfected pool in low metal concentration for two weeks, sort for cells of high-fluorescence, and allow for recovery and proliferation. It is then possible to amplify gene expression by culturing the clones in increasing metal, resulting in further improvement of recombinant protein expression.
37
Nordling, Torbjörn E. M. "Robust inference of gene regulatory networks : System properties, variable selection, subnetworks, and design of experiments." Doctoral thesis, KTH, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120830.
Testo completoAbstract (sommario):
In this thesis, inference of biological networks from in vivo data generated by perturbation experiments is considered, i.e. deduction of causal interactions that exist among the observed variables. Knowledge of such regulatory influences is essential in biology. A system property–interampatteness–is introduced that explains why the variation in existing gene expression data is concentrated to a few “characteristic modes” or “eigengenes”, and why previously inferred models have a large number of false positive and false negative links. An interampatte system is characterized by strong INTERactions enabling simultaneous AMPlification and ATTEnuation of different signals and we show that perturbation of individual state variables, e.g. genes, typically leads to ill-conditioned data with both characteristic and weak modes. The weak modes are typically dominated by measurement noise due to poor excitation and their existence hampers network reconstruction. The excitation problem is solved by iterative design of correlated multi-gene perturbation experiments that counteract the intrinsic signal attenuation of the system. The next perturbation should be designed such that the expected response practically spans an additional dimension of the state space. The proposed design is numerically demonstrated for the Snf1 signalling pathway in S. cerevisiae. The impact of unperturbed and unobserved latent state variables, that exist in any real biological system, on the inferred network and required set-up of the experiments for network inference is analysed. Their existence implies that a subnetwork of pseudo-direct causal regulatory influences, accounting for all environmental effects, in general is inferred. In principle, the number of latent states and different paths between the nodes of the network can be estimated, but their identity cannot be determined unless they are observed or perturbed directly. Network inference is recognized as a variable/model selection problem and solved by considering all possible models of a specified class that can explain the data at a desired significance level, and by classifying only the links present in all of these models as existing. As shown, these links can be determined without any parameter estimation by reformulating the variable selection problem as a robust rank problem. Solution of the rank problem enable assignment of confidence to individual interactions, without resorting to any approximation or asymptotic results. This is demonstrated by reverse engineering of the synthetic IRMA gene regulatory network from published data. A previously unknown activation of transcription of SWI5 by CBF1 in the IRMA strain of S. cerevisiae is proven to exist, which serves to illustrate that even the accumulated knowledge of well studied genes is incomplete.<br>Denna avhandling behandlar inferens av biologiskanätverk från in vivo data genererat genom störningsexperiment, d.v.s. bestämning av kausala kopplingar som existerar mellan de observerade variablerna. Kunskap om dessa regulatoriska influenser är väsentlig för biologisk förståelse. En system egenskap—förstärksvagning—introduceras. Denna förklarar varför variationen i existerande genexpressionsdata är koncentrerat till några få ”karakteristiska moder” eller ”egengener” och varför de modeller som konstruerats innan innehåller många falska positiva och falska negativa linkar. Ett system med förstärksvagning karakteriseras av starka kopplingar som möjliggör simultan FÖRSTÄRKning och förSVAGNING av olika signaler. Vi demonstrerar att störning av individuella tillståndsvariabler, t.ex. gener, typiskt leder till illakonditionerat data med både karakteristiska och svaga moder. De svaga moderna domineras typiskt av mätbrus p.g.a. dålig excitering och försvårar rekonstruktion av nätverket. Excitationsproblemet löses med iterativdesign av experiment där korrelerade störningar i multipla gener motverkar systemets inneboende försvagning av signaller. Följande störning bör designas så att det förväntade svaret praktiskt spänner ytterligare en dimension av tillståndsrummet. Den föreslagna designen demonstreras numeriskt för Snf1 signalleringsvägen i S. cerevisiae. Påverkan av ostörda och icke observerade latenta tillståndsvariabler, som existerar i varje verkligt biologiskt system, på konstruerade nätverk och planeringen av experiment för nätverksinferens analyseras. Existens av dessa tillståndsvariabler innebär att delnätverk med pseudo-direkta regulatoriska influenser, som kompenserar för miljöeffekter, generellt bestäms. I princip så kan antalet latenta tillstånd och alternativa vägar mellan noder i nätverket bestämmas, men deras identitet kan ej bestämmas om de inte direkt observeras eller störs. Nätverksinferens behandlas som ett variabel-/modelselektionsproblem och löses genom att undersöka alla modeller inom en vald klass som kan förklara datat på den önskade signifikansnivån, samt klassificera endast linkar som är närvarande i alla dessa modeller som existerande. Dessa linkar kan bestämmas utan estimering av parametrar genom att skriva om variabelselektionsproblemet som ett robustrangproblem. Lösning av rangproblemet möjliggör att statistisk konfidens kan tillskrivas individuella linkar utan approximationer eller asymptotiska betraktningar. Detta demonstreras genom rekonstruktion av det syntetiska IRMA genreglernätverket från publicerat data. En tidigare okänd aktivering av transkription av SWI5 av CBF1 i IRMA stammen av S. cerevisiae bevisas. Detta illustrerar att t.o.m. den ackumulerade kunskapen om välstuderade gener är ofullständig.<br><p>QC 20130508</p>
38
Bueno, Maria Rita Spina. "Níveis de seleção: uma avaliação a partir da teoria do \"gene egoísta\"." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/8/8133/tde-03092009-145224/.
Testo completoAbstract (sommario):
Esta dissertação de mestrado aborda a controvérsia em torno de qual é o nível biológico no qual a seleção natural atua, com ênfase na proposta de Richard Dawkins do gene egoísta e nas questões que surgem em torno da mesma. Examina-se um panorama de questões de filosofia da biologia abordadas a partir do problema dos níveis nos quais a seleção natural atua. Esperamos que ao avaliar o impacto da teoria do gene egoísta na problemática evolutiva, consigamos compreender sua importância. O objetivo deste trabalho é filosófico, delineando as questões e clarificando alguns termos do debate, sem se propor a tomar partido por uma ou outra posição. O primeiro capítulo apresenta as origens históricas do debate, partindo do ponto de vista original de Charles Darwin no qual o indivíduo era a entidade efetivamente selecionada. Em seguida, buscamos entender como novas questões empíricas, em especial a busca de explicações biológicas para o altruísmo, conduziram a propostas de seleção de grupo. No segundo capítulo delineamos como o desenvolvimento da genética possibilitou que um novo nível de seleção fosse proposto: o gene, e acompanhamos a exposição de Dawkins sobre o ponto de vista do gene egoísta, em especial a partir de seus dois livros mais relevantes sobre o tema: O gene egoísta e O fenótipo estendido. O terceiro capítulo examina diversas aproximações filosóficas no contexto de resposta à pergunta: o que é uma unidade de seleção?. Nosso estudo é consistente com a tese de que as forças seletivas atuam simultaneamente em diversos níveis.<br>This Masters thesis studies the controversy over what is the biological level in which natural selection takes place. Emphasis is given to Richard Dawkins proposal of the selfish gene and to the issues that arise therefrom, which include many questions in the philosophy of biology. We hope that by assessing the impact that the theory of the selfish gene has had on the problems of evolution, one may understand its importance. The aim of this study is philosophical, raising questions and clarifying the terms of the debate, without taking side on one or another position. The first chapter presents the historical origins of the debate, starting with the original view of Charles Darwin that the individual is the entity that is effectively selected. We then set out to understand how new empirical problems, specifically the search for biological explanations for altruism, led to proposals of group selection. In the second chapter, we depict how the development of genetics allowed that a new level of selection be proposed: the gene. We analyze Dawkins exposition of the point of view of the selfish gene, especially in the two most important books on the subject: The selfish gene and The extended phenotype. The third chapter examines several philosophical approaches to the question what is a unit of selection?. Our study is consistent with the thesis that selective forces act simultaneously in different levels.
39
Zhang, Yiran. "Bayesian Variable Selection for High-Dimensional Data with an Ordinal Response." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1565283865507018.
Testo completo
40
Tjärnberg, Andreas. "Exploring the Boundaries of Gene Regulatory Network Inference." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122149.
Testo completoAbstract (sommario):
To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference.<br><p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p><p> </p>
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Broc, Camilo. "Variable selection for data aggregated from different sources with group of variable structure." Thesis, Pau, 2019. http://www.theses.fr/2019PAUU3048.
Testo completoAbstract (sommario):
Durant les dernières décennies, la quantité de données disponibles en génétique a consi-dérablement augmenté. D’une part, une amélioration des technologies de séquençage demolécules a permis de réduire fortement le coût d’extraction du génome humain. D’autrepart, des consortiums internationaux d’institutions ont permis la mise en commun de lacollecte de données sur de larges populations. Cette quantité de données nous permetd’espérer mieux comprendre les mécanismes régissant le fonctionnement de nos cellules.Dans ce contexte, l’épidémiologie génétique est un domaine cherchant à déterminer larelation entre des caractéristiques génétiques et l’apparition d’une maladie. Des méthodesstatistiques spécifiques à ce domaine ont dû être développées, en particulier à cause desdimensions que les données présentent : en génétique, l’information est contenue dans unnombre de variables grand par rapport au nombre d’observations.Dans cette dissertation, deux contributions sont présentées. Le premier projet appeléPIGE (Pathway-Interaction Gene Environment) développe une méthode pour déterminerdes interactions gène-environnement. Le second projet vise à développer une méthode desélection de variables adaptée à l’analyse de données provenant de différentes études etprésentant une structure de groupe de variables.Le document est divisé en six parties. Le premier chapitre met en relief le contexte,d’un point de vue à la fois biologique et mathématique. Le deuxième chapitre présente lesmotivations de ce travail et la mise en œuvre d’études en épidémiologie génétique. Le troi-sième chapitre aborde les questions relatives à l’analyse d’interactions gène-environnementet la première contribution de la thèse y est présentée. Le quatrième chapitre traite desproblématiques de méta-analyses. Le développement d’une nouvelle méthode de réductionde dimension répondant à ces questions y est présenté. Le cinquième chapitre met en avantla pertinence de la méthode dans des cas de pleiotropie. Enfin, le sixième et dernier chapitredresse un bilan du travail présenté et dresse des perspectives pour le futur<br>During the last decades, the amount of available genetic data on populations has growndrastically. From one side, a refinement of chemical technologies have made possible theextraction of the human genome of individuals at an accessible cost. From the other side,consortia of institutions and laboratories around the world have permitted the collectionof data on a variety of individuals and population. This amount of data raised hope onour ability to understand the deepest mechanisms involved in the functioning of our cells.Notably, genetic epidemiology is a field that studies the relation between the geneticfeatures and the onset of a disease. Specific statistical methods have been necessary forthose analyses, especially due to the dimensions of available data: in genetics, informationis contained in a high number of variables compared to the number of observations.In this dissertation, two contributions are presented. The first project called PIGE (Pathway-Interaction Gene Environment) deals with gene-environment interaction assessments.The second one aims at developing variable selection methods for data which has groupstructures in both the variables and the observations.The document is divided into six chapters. The first chapter sets the background of this work,where both biological and mathematical notations and concepts are presented and gives ahistory of the motivation behind genetics and genetic epidemiology. The second chapterpresent an overview of the statistical methods currently in use for genetic epidemiology.The third chapter deals with the identification of gene-environment interactions. It includesa presentation of existing approaches for this problem and a contribution of the thesis. Thefourth chapter brings off the problem of meta-analysis. A definition of the problem and anoverview of the existing approaches are presented. Then, a new approach is introduced.The fifth chapter explains the pleiotropy studies and how the method presented in theprevious chapter is suited for this kind of analysis. The last chapter compiles conclusionsand research lines for the future
42
Ha, Sook Shin. "Dimensionality Reduction, Feature Selection and Visualization of Biological Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77169.
Testo completoAbstract (sommario):
Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological insight. Thus, dimensionality reduction becomes an imperative pre-processing step in analyzing and visualizing high-dimensional biological data. Two major approaches to dimensionality reduction in genomic analysis and biomarker identification studies are: Feature extraction, creating new features by combining existing ones based on a mapping technique; and feature selection, choosing an optimal subset of all features based on an objective function. In this dissertation, we show how our innovative reduction schemes effectively reduce the dimensionality of DNA gene expression data to extract biologically interpretable and relevant features which result in enhancing the biomarker identification process.
To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) subtypes classification, we extract molecular features from MD microarray data by constructing sub-networks using a novel integrative scheme which utilizes protein-protein interaction (PPI) network, functional gene sets information and mRNA profiling data. The workflow includes three major steps: First, by combining PPI network structure and gene-gene co-expression relationship into a new distance metric, we apply affinity propagation clustering (APC) to build gene sub-networks; secondly, we further incorporate functional gene sets knowledge to complement the physical interaction information; finally, based on the constructed sub-network and gene set features, we apply multi-class support vector machine (MSVM) for MD sub-type classification and highlight the biomarkers contributing to the sub-type prediction. The experimental results show that our scheme could construct sub-networks that are more relevant to MD than those constructed by the conventional approach. Furthermore, our integrative strategy substantially improved the prediction accuracy, especially for those ‘hard-to-classify' sub-types.
Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proven incorrect and applying uniform weight in the pathway analysis may not be an adequate approach for tasks like molecular classification of diseases, as genes in a functional group may have different differential power. Hence, we propose to use different weights for the pathway analysis which resulted in the development of four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.
To help us understand our MD expression data better and derive scientific insight from it, we have explored a suite of visualization tools. Particularly, for selected top performing MD sub-networks, we displayed the network view using Cytoscape; functional annotations using IPA and DAVID functional analysis tools; expression pattern using heat-map and parallel coordinates plot; and MD associated pathways using KEGG pathway diagrams. We also performed weighted MD pathway analysis, and identified overlapping sub-networks across different weight schemes and different MD subtypes using Venn Diagrams, which resulted in the identification of a new sub-network significantly associated with MD. All those graphically displayed data and information helped us understand our MD data and the MD subtypes better, resulting in the identification of several potentially MD associated biomarker pathways and genes.<br>Ph. D.
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Kontos, Kevin. "Gaussian graphical model selection for gene regulatory network reverse engineering and function prediction." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210301.
Testo completoAbstract (sommario):
One of the most important and challenging ``knowledge extraction' tasks in bioinformatics is the reverse engineering of gene regulatory networks (GRNs) from DNA microarray gene expression data. Indeed, as a result of the development of high-throughput data-collection techniques, biology is experiencing a data flood phenomenon that pushes biologists toward a new view of biology--systems biology--that aims at system-level understanding of biological systems.<p><p>Unfortunately, even for small model organisms such as the yeast Saccharomyces cerevisiae, the number p of genes is much larger than the number n of expression data samples. The dimensionality issue induced by this ``small n, large p' data setting renders standard statistical learning methods inadequate. Restricting the complexity of the models enables to deal with this serious impediment. Indeed, by introducing (a priori undesirable) bias in the model selection procedure, one reduces the variance of the selected model thereby increasing its accuracy.<p><p>Gaussian graphical models (GGMs) have proven to be a very powerful formalism to infer GRNs from expression data. Standard GGM selection techniques can unfortunately not be used in the ``small n, large p' data setting. One way to overcome this issue is to resort to regularization. In particular, shrinkage estimators of the covariance matrix--required to infer GGMs--have proven to be very effective. Our first contribution consists in a new shrinkage estimator that improves upon existing ones through the use of a Monte Carlo (parametric bootstrap) procedure.<p><p>Another approach to GGM selection in the ``small n, large p' data setting consists in reverse engineering limited-order partial correlation graphs (q-partial correlation graphs) to approximate GGMs. Our second contribution consists in an inference algorithm, the q-nested procedure, that builds a sequence of nested q-partial correlation graphs to take advantage of the smaller order graphs' topology to infer higher order graphs. This allows us to significantly speed up the inference of such graphs and to avoid problems related to multiple testing. Consequently, we are able to consider higher order graphs, thereby increasing the accuracy of the inferred graphs.<p><p>Another important challenge in bioinformatics is the prediction of gene function. An example of such a prediction task is the identification of genes that are targets of the nitrogen catabolite repression (NCR) selection mechanism in the yeast Saccharomyces cerevisiae. The study of model organisms such as Saccharomyces cerevisiae is indispensable for the understanding of more complex organisms. Our third contribution consists in extending the standard two-class classification approach by enriching the set of variables and comparing several feature selection techniques and classification algorithms.<p><p>Finally, our fourth contribution formulates the prediction of NCR target genes as a network inference task. We use GGM selection to infer multivariate dependencies between genes, and, starting from a set of genes known to be sensitive to NCR, we classify the remaining genes. We hence avoid problems related to the choice of a negative training set and take advantage of the robustness of GGM selection techniques in the ``small n, large p' data setting.<br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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Peng, Ho-Lan, and 彭郃嵐. "Gene Selection Methods." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/03200922452706313969.
Testo completoAbstract (sommario):
碩士<br>國立交通大學<br>統計學研究所<br>96<br>It's a trend to use statistical methods in medical science. If the genes which cause the diseases could be found, it might be helpful to nowadays medical field. In this article, we proposed several methods to find the probable influential genes which are over- or down-expressed in some but not all samples in a disease group. Those methods include WORT (weight outlier robust t-statistic), WOS (weight outlier sum), PGM (the MLE of probability of Gaussian mixture model), TGM (T-statistic of Gaussian mixture model), QGM(Quantile of Gaussian mixture model), and Bayesian Rule P-value(BRP). Also we will compare those methods with four methods (t-statistic, OS, ORT, COPA) which have been proposed and published for detecting differentially expressed genes. Those new methods include improvements of ORT and OS methods, four methods related to Gaussian mixture model and Bayesian method.
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Chang, Shu-Jing, and 張淑淨. "Nonlinear Gene Selection Method." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/8ugnmr.
Testo completoAbstract (sommario):
碩士<br>國立交通大學<br>統計學研究所<br>92<br>Microarray data contains large number of p genes (usually several thousands) and small number of n patients (usually nearly 100 or less). The problem of identifying the features best discriminate among the classes to improve the ability of a classifier is known as feature selection. Some current feature selection methods and the problem of dealing with "large p, small n" are reviewed. The Support Vector Machines (SVMs) has proofed excellent performance in practice as a classification methodology. For linear classification problem, this paper studies the following two issues: (i) the number of one gene s surrogates somehow affects the importance of the gene; (ii) the case of overlapping classes. For nonlinear classification problem, we utilize two procedures: 1. mapping the original nonlinear separable data to the high dimension space, and then use SVM RFE with linear kernel to find crucial genes; 2. using SVM RFE with nonlinear kernel. Then we compare these two methods on nonlinear toy problem.
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Tseng, Yen-Cheng, and 曾彥誠. "Improving Target Gene Selection in Microarray Data of Ovarian CancerImproving Target Gene Selection in Microarray Data of Ovarian CancerImproving Target Gene Selection in Microarray Data of Ovarian CancerImproving Target Gene Selection in Microarray." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/02746016456521864581.
Testo completoAbstract (sommario):
碩士<br>華梵大學<br>資訊管理學系碩士班<br>97<br>The Cancers may be one of the nightmares of the humanity in advances into 22 century. Furthermore, the increase speed of cancer has surpassed the scope which the humanity can understand. Taking this into consideration, scientists have transformed passive role into the steer seeker in the domain of biomedicine. Because of precious pathology material and the mutually union project of Human Genome, it is possible to find the production reason of cancer cell and the rule in the humanity's huge gene labyrinth. Without definite reasons, the ovarian cancer is one of common gynecology cancers. In this paper, the computation intelligence with decision tree can effectively get the target genes. Due to obtained target genes, doctors can effectively use these pathology data to achieve the cancer effective prevention.
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Lu, Yu-Lun, and 陸宇綸. "Gene Expression Normalization by GO based Housekeeping Gene Selection." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/xgd29k.
Testo completoAbstract (sommario):
碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>102<br>High throughput RNA-seq analysis provides a powerful tool for revealing relationships between gene expression level and biological function of proteins. To discover differentially expressed genes among various RNA-seq datasets obtained from different experimental designs, an appropriate normalization method for calibrating multiple experimental datasets is the first challenging problem. In this thesis, a novel normalization method to facilitate biologists in selecting a set of suitable housekeeping genes for inter-sample normalization is proposed. The approach is achieved by adopting user defined experimentally related keywords, gene ontology (GO) annotations, orthologous housekeeping genes, and stability of housekeeping genes at different time periods. By identifying the most distanced GO terms from query keywords and selecting housekeeping gene candidates with low coefficients of variation among different spatio-temporal datasets, the proposed method can automatically enumerate a set of functionally irrelevant housekeeping genes for practical normalization. By employing benchmark RNA-seq datasets to evaluate our developed system, the results showed that different selections of housekeeping gene set would lead to strong impact on differential gene expression analysis. The compared results have shown that our proposed method outperformed other traditional approaches in terms of both sensitivity and specificity. The proposed mechanism of selecting appropriate housekeeping genes for inter-dataset normalization is robust and accurate for differential expression analyses.
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Chen, Yu-Chao, and 陳昱超. "Key Gene Selection in Microarray Using Sequential Forward Selection Strategy." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/7c578t.
Testo completoAbstract (sommario):
碩士<br>國立臺中科技大學<br>流通管理系碩士班<br>101<br>High dimension of feature space、low instance amount、and only a limited number of key genes critical for bioinformation classification problems are three characteristics in the analysis of microarray. On one hand, the selection of discriminative genes is important. On the other hand, a collection of discriminative genes do not necessarily lead to good classification quality. This is because some attributes could likely possess the similar classification effects and in turn lead to the redundant classification results. In order to generate the subsets of genes with not only sufficient but also necessary discrimination power for bioinformation classification problems, a novel selection strategy which integrates fuzzy cluster analyses and information gain (IG) into the traditional sequential forward selection (SFS) algorithm is proposed in this paper. In terms of classification accuracy and discrimination power, the experimental results gained from six microarray datasets show that our strategy can efficiently select compact subsets of characterizing genes and these selected genes are suitable for various conventional classifiers.
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Swartz, Michael D. "Stochastic search gene suggestion: Hierarchical Bayesian model selection meets gene mapping." Thesis, 2004. http://hdl.handle.net/1911/18711.
Testo completoAbstract (sommario):
This dissertation introduces a novel approach for addressing the complexities of mapping a complex disease by adjusting a Bayesian Model Selection method. Mapping the genes for a complex disease, such as Rheumatoid Arthritis, involves finding multiple genetic loci that may contribute to the onset of the disease. Pairwise testing of the loci leads to the problem of multiple testing. To avoid multiple tests, one can look at haplotypes, or linear sets of loci, but this results in a contingency table with sparse counts, especially when using marker loci with multiple alleles. In order to jointly consider all loci in the problem, we applied a Hierarchical Bayesian Model Selection method to a conditional logistic regression model used in gene mapping. We chose Stochastic Search Variable Selection for its use of latent indicator variables to indicate those covariates, in this case genes, important to the model. We extended the latent variable structure to mirror genetics through a latent allele indicator conditional on a latent locus indicator. We also examined using a prior correlation structure on the allele coefficients that mirrors linkage disequilibrium, a between-locus genetic correlation structure. Ultimately, we ruled out the usefulness of a dependent covariance structure on the prior for allele main effects, but we developed a preliminary method of fitting a positive definite matrix to data based on adjusting the kriging covariance functions commonly used in geostatistics or spatial statistics. We developed a Metropolis-within-Gibbs algorithm to sample our gene suggestion posterior, and evaluated the algorithm's performance on simulated data and completed the research with application to real data, searching for genes associated with Rheumatoid Arthritis. On simulated data, we found that our method successfully recognized disease loci and nondisease loci. Despite complications when analyzing the real data, our method did indicate the genes more strongly associated with Rheumatoid Arthritis.
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Ke, Chao-Hsuan, and 柯兆軒. "Two-Stage Gene Selection Algorithms for Classification of Gene Expression Data." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/85395768415812242759.
Testo completoAbstract (sommario):
碩士<br>國立高雄應用科技大學<br>電子與資訊工程研究所碩士班<br>96<br>The microarray is a medical diagnostic tool with good efficiency, and it was used for analyzing the behavior characteristic between the gene and disease by the extensive one at present. Microarray data are characterized by a high dimension, which could be analyzed more than thousand of genes and diseases simultaneously. However, it will lead to need more computation time when it is implemented on classification. Many previous literatures showed the feature (gene) selection has some advantage, such as gene extraction which influences classification accuracy effectively, to eliminate the useless genes and improve the calculation performance and classification accuracy. The goal of this study is to select a small set of genes which are useful to the classification task. We proposed a two-stage method using several filter methods to proceed gene ranking and combined the evolutional algorithms on gene expression data to select an optimal gene subset. In this study, an improved particle swarm optimization which introduced a Boolean function was used to improve the disadvantage of standard binary particle swarm optimization as a new evolutional algorithm for gene selection, and both k-nearest neighbor and support vector machine classifiers were used to calculate the classification accuracy. The experimental results revealed that our proposed feature selection method is able to effectively select the relevant gene subset and achieve better classification accuracy than the previous studies.