Thèses sur le sujet « Genetic data »
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Qiao, Dandi. « Statistical Approaches for Next-Generation Sequencing Data ». Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10689.
Texte intégralHaroun, Paul. « Genetic algorithm and data visualization ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0017/MQ37125.pdf.
Texte intégralLankhorst, Marc Martijn. « Genetic algorithms in data analysis ». [S.l. : [Groningen] : s.n.] ; [University Library Groningen] [Host], 1996. http://irs.ub.rug.nl/ppn/142964662.
Texte intégralHiden, Hugo George. « Data-based modelling using genetic programming ». Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246137.
Texte intégralAuton, Adam. « The estimation of recombination rates from population genetic data ». Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:dc38045b-725d-4afc-8c76-94769db3534d.
Texte intégralAgarwala, Vineeta. « Integrating empirical data and population genetic simulations to study the genetic architecture of type 2 diabetes ». Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11120.
Texte intégralRomano, 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/.
Texte intégralLi, Xin. « Haplotype Inference from Pedigree Data and Population Data ». Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1259867573.
Texte intégralTitle from PDF (viewed on 2009-12-30) Department of Electrical Engineering and Computer Science Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center
Shenoy, U. Nagaraj. « Automatic Data Partitioning By Hierarchical Genetic Search ». Thesis, Indian Institute of Science, 1996. http://hdl.handle.net/2005/172.
Texte intégralThe introduction of languages like High Performance Fortran (HPF) which allow the programmer to indicate how the arrays used in the program have to be distributed across the local memories of a multi-computer has not completely unburdened the parallel programmer from the intricacies of these architectures. In order to tap the full potential of these architectures, the compiler has to perform this crucial task of data partitioning automatically. This would not only unburden the programmer but would make the programs more efficient since the compiler can be made more intelligent to take care of the architectural nuances. The topic of this thesis namely the automatic data partitioning deals with finding the best data partition for the various arrays used in the entire program in such a way that the cost of execution of the entire program is minimized. The compiler could resort to runtime redistribution of the arrays at various points in the program if found profitable. Several aspects of this problem have been proven to be NP-complete. Other researchers have suggested heuristic solutions to solve this problem. In this thesis we propose a genetic algorithm namely the Hierarchical Genetic Search algorithm to solve this problem.
Al-Madi, Naila Shikri. « Improved Genetic Programming Techniques For Data Classification ». Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27097.
Texte intégralMcCaskie, Pamela Ann. « Multiple-imputation approaches to haplotypic analysis of population-based data with applications to cardiovascular disease ». University of Western Australia. School of Population Health, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0160.
Texte intégralCole, Rowena Marie. « Clustering with genetic algorithms ». University of Western Australia. Dept. of Computer Science, 1998. http://theses.library.uwa.edu.au/adt-WU2003.0008.
Texte intégralAhsan, Nasir Computer Science & Engineering Faculty of Engineering UNSW. « Learning causal networks from gene expression data ». Awarded by:University of New South Wales. School of Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/26151.
Texte intégralLi, Xiang, et xiali@cs rmit edu au. « Utilising Restricted For-Loops in Genetic Programming ». RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080110.122751.
Texte intégralLei, Celestino. « Using genetic algorithms and boosting for data preprocessing ». Thesis, University of Macau, 2002. http://umaclib3.umac.mo/record=b1447848.
Texte intégralLeslie, Stephen. « Inference of Population Stratification Using Population Genetic Data ». Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504423.
Texte intégralCheng, Lulu. « Statistical Methods for Genetic Pathway-Based Data Analysis ». Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/52039.
Texte intégralPh. D.
Liu, Dongqing. « GENETIC ALGORITHMS FOR SAMPLE CLASSIFICATION OF MICROARRAY DATA ». University of Akron / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=akron1125253420.
Texte intégralDelman, Bethany. « Genetic algorithms in cryptography / ». Link to online version, 2003. https://ritdml.rit.edu/dspace/handle/1850/263.
Texte intégralXia, Fan, et 夏凡. « Some topics on statistical analysis of genetic imprinting data and microbiome compositional data ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206673.
Texte intégralpublished_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
Uduman, Mohamed. « Identifying the largest complete data set from ALFRED / ». Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/1876.
Texte intégralRomano, Eduardo. « Selection indices for combining marker genetic data and animal model information ». Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/44879.
Texte intégralMaster of Science
Stewart, William C. L. « Alternative models for estimating genetic maps from pedigree data / ». Thesis, Connect to this title online ; UW restricted, 2005. http://hdl.handle.net/1773/8975.
Texte intégralJochumsson, Thorvaldur. « Inferring Genetic Networks from Expression Data with Mutual Information ». Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-736.
Texte intégralRecent methods to infer genetic networks are based on identifying gene interactions by similarities in expression profiles. These methods are founded on the assumption that interacting genes share higher similarities in their expression profiles than non-interacting genes. In this dissertation this assumption is validated when using mutual information as a similarity measure. Three algorithms that calculate mutual information between expression data are developed: 1) a basic approach implemented with the histogram technique; 2) an extension of the basic approach that takes into consideration time delay between expression profiles; 3) an extension of the basic approach that takes into consideration that genes are regulated in a complex manner by multiple genes. In our experiments we compare the mutual information distributions for profiles of interacting and non-interacting genes. The results show that interacting genes do not share higher mutual information in their expression profiles than non-interacting genes, thus contradicting the basic assumption that similarity measures need to fulfil. This indicates that mutual information is not appropriate as similarity measure, which contradicts earlier proposals.
Ayaz, Eyup Serdar. « Resonctructing Signaling Pathways From Rnai Data Using Genetic Algorithms ». Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613813/index.pdf.
Texte intégralLiu, Kejun. « Software and Methods for Analyzing Molecular Genetic Marker Data ». NCSU, 2003. http://www.lib.ncsu.edu/theses/available/etd-07182003-122001/.
Texte intégralRomero, Carol Eduardo. « A genetic algorithm for reservoir characterisation using production data ». Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394511.
Texte intégralTong, Dong Ling. « Genetic algorithm-neural network : feature extraction for bioinformatics data ». Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.
Texte intégralCarter, Jason W. « Testing effectiveness of genetic algorithms for exploratory data analysis ». Thesis, Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/9065.
Texte intégralHeuristic methods of solving exploratory data analysis problems suffer from one major weakness - uncertainty regarding the optimality of the results. The developers of DaMI (Data Mining Initiative), a genetic algorithm designed to mine the CCEP (Comprehensive Clinical Evaluation Program) database in the search for a Persian Gulf War syndrome, proposed a method to overcome this weakness: reproducibility -- the conjecture that consistent convergence on the same solutions is both necessary and sufficient to ensure a genetic algorithm has effectively searched an unknown solution space. We demonstrate the weakness of this conjecture in light of accepted genetic algorithm theory. We then test the conjecture by modifying the CCEP database with the insertion of an interesting solution of known quality and performing a discovery session using DaMI on this modified database. The necessity of reproducibility as a terminating condition is falsified by the algorithm finding the optimal solution without yielding strong reproducibility. The sufficiency of reproducibility as a terminating condition is analyzed by manual examination of the CCEP database in which strong reproducibility was experienced. Ex post facto knowledge of the solution space is used to prove that DaMI had not found the optimal solutions though it gave strong reproducibility, causing us to reject the conjecture that strong reproducibile is a sufficient terminating condition.
Lin, Xinyi (Cindy). « Statistical Methods for High-Dimensional Data in Genetic Epidemiology ». Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11326.
Texte intégralLi, Qiao. « Data mining and statistical techniques applied to genetic epidemiology ». Thesis, University of East Anglia, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533716.
Texte intégralALMEIDA, MANOEL ROBERTO AGUIRRE DE. « HIBRID NEURO-FUZZY-GENETIC SYSTEM FOR AUTOMATIC DATA MINING ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5303@1.
Texte intégralEsta dissertação apresenta a proposta e o desenvolvimento de um sistema de mineração de dados inteiramente automático. O objetivo principal é criar um sistema que seja capaz de realizar a extração de informações obscuras a partir de bases de dados complexas, sem exigir a presença de um especialista técnico para configurá-lo. O sistema híbrido neuro-fuzzy hierárquico com particionamento binário (NFHB) vem apresentando excelentes resultados em tarefas de classificação de padrões e previsão, além de possuir importantes características não encontradas em outros sistemas similares, entre elas: aprendizado automático de sua estrutura; capacidade de receber um número maior de entradas abrangendo um maior número de aplicações; e geração de regras lingüísticas como produto de seu treinamento. Entretanto, este modelo ainda necessita de uma complexa parametrização inicial antes de seu treinamento, impedindo que o processo seja automático em sua totalidade. O novo modelo proposto busca otimizar a parametrização do sistema NFHB utilizando a técnica de coevolução genética, criando assim um novo sistema de mineração de dados completamente automático. O trabalho foi realizado em quatro partes principais: avaliação de sistemas existentes utilizados na mineração de dados; estudo do sistema NFHB e a determinação de seus principais parâmetros; desenvolvimento do sistema híbrido neuro-fuzzy-genético automático para mineração de dados; e o estudo de casos. No estudo dos sistemas existentes para mineração de dados buscou-se encontrar algum modelo que apresentasse bons resultados e ainda fosse passível de automatização. Várias técnicas foram estudadas, entre elas: Métodos Estatísticos, Árvores de Decisão, Associação de Regras, Algoritmos Genéticos, Redes Neurais Artificiais, Sistemas Fuzzy e Sistemas Neuro-Fuzzy. O sistema NFHB foi escolhido como sistema de inferência e extração de regras para a realização da mineração de dados. Deste modo, este modelo foi estudado e seus parâmetros mais importantes foram determinados. Além disso, técnicas de seleção de variáveis de entradas foram investigadas para servirem como opções para o modelo. Ao final, foi obtido um conjunto de parâmetros que deve ser automaticamente determinado para a completa configuração deste sistema. Um modelo coevolutivo genético hierárquico foi criado para realizar com excelência a tarefa de otimização do sistema NFHB. Desta forma, foi modelada uma arquitetura hierárquica de Algoritmos Genéticos (AG s), onde os mesmos realizam tarefas de otimização complementares. Nesta etapa, também foram determinados os melhores operadores genéticos, a parametrização dos AG s, a melhor representação dos cromossomas e as funções de avaliação. O melhor conjunto de parâmetros encontrado é utilizado na configuração do NFHB, tornando o processo inteiramente automático. No estudo de casos, vários testes foram realizados em bases de dados reais e do tipo benchmark. Para problemas de previsão, foram utilizadas séries de carga de energia elétrica de seis empresas: Cerj, Copel, Eletropaulo, Cemig, Furnas e Light. Na área de classificação de padrões, foram utilizadas bases conhecidas de vários artigos da área como Glass Data, Wine Data, Bupa Liver Disorders e Pima Indian Diabetes. Após a realização dos testes, foi feita uma comparação com os resultados obtidos por vários algoritmos e pelo NFHB original, porém com parâmetros determinados por um especialista. Os testes mostraram que o modelo criado obteve resultados bastante satisfatórios, pois foi possível, com um processo completamente automático, obter taxas de erro semelhantes às obtidas por um especialista, e em alguns casos taxas menores. Desta forma, um usuário do sistema, sem qualquer conhecimento técnico sobre os modelos utilizados, pode utilizá-lo para realizar min
This dissertation presents the proposal and the development of a totally automatic data mining system. The main objective is to create a system that is capable of extracting obscure information from complex databases, without demanding the presence of a technical specialist to configure it. The Hierarchical Neuro-Fuzzy Binary Space Partitioning model (NFHB) has produced excellent results in pattern classification and time series forecasting tasks. Additionally, it provides important features that are not present in other similar systems, such as: automatic learning of its structure; ability to deal with a larger number of input variables, thus increasing the range of possible applications; and generation of linguistic rules as a result of its training process. However, this model depends on a complex configuration process before the training is performed, hindering to achieve a totally automatic system. The model proposed in this Dissertation tries to optimize the NFHB system parameters by using the genetic coevolution technique, thus creating a new automatic data mining system. This work consisted of four main parts: evaluation of existing systems used in data mining; study of the NFHB system and definition of its main parameters; development of the automatic hybrid neuro-fuzzy-genetic system for data mining; and case studies. In the study of existing data mining systems, the aim was to find a suitable model that could yield good results and still be automated. Several techniques have been studied, among them: Statistical methods, Decision Trees, Rules Association, Genetic Algorithms, Artificial Neural Networks, Fuzzy and Neuro- Fuzzy Systems. The NFHB System was chosen for inference and rule extraction in the data mining process. In this way, this model was carefully studied and its most important parameters were determined. Moreover, input variable selection techniques were investigated, to be used with the proposed model. Finally, a set of parameters was defined, which must be determined automatically for the complete system configuration. A hierarchical coevolutive genetic model was created to execute the system optimization task with efficiency. Therefore, a hierarchical architecture of genetic algorithms (GAs) was created, where the GAs execute complementary optimization tasks. In this stage, the best genetic operators, the GAs configuration, the chromossomes representation, and evaluation functions were also determined. The best set of parameters found was used in the NFHB configuration, making the process entirely automatic. In the case studies, various tests were performed with benchmark databases. For forecasting problems, six electric load series were used: Cerj, Copel, Eletropaulo, Cemig, Furnas and Light. In the pattern classification area, some well known databases were used, namely Glass Data, Wine Data, Bupa Liver Disorders and Pima Indian Diabetes. After the tests were carried out, a comparison was made with known models and with the original NFHB System, configured by a specialist. The tests have demonstrated that the proposed model generates satisfactory results, producing, with an automatic process, similar errors to the ones obtained with a specialist configuration, and, in some cases, even better results can be obtained. Therefore, a user without any technical knowledge of the system, can use it to perform data mining, extracting information and knowledge that can help him/her in decision taking processes, which is the final objective of a Knowledge Data Discovery process.
MEDEIROS, SHELLY CRISTIANE DAVILA. « INVERSION OF PARAMETERS IN SEISMIC DATA BY GENETIC ALGORITHMS ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8622@1.
Texte intégralEsta dissertação investiga o uso de Algoritmos Genéticos aplicados em dados sísmicos com o objetivo de obter parâmetros físicos e atributos sísmicos que auxiliem na caracterização das rochas de um subsolo terrestre. Os dados sísmicos têm sido extensamente empregados no setor de exploração de petróleo. As aplicações envolvendo sísmica não se restringem na busca por novas reservas de petróleo, mas também são usadas para projetar novos poços e melhorar a produção dos reservatórios de petróleo. O levantamento de dados sísmicos permite analisar extensas áreas da subsuperfície com custo praticável em relação a outras técnicas. Entretanto, a interpretação desses dados com o objetivo de obter informações relevantes e acuradas não é uma tarefa simples. Para isto, várias técnicas de inversão sísmica vêm sendo desenvolvidas. Este trabalho consistiu em avaliar uma alternativa que emprega Algoritmos Genéticos para inverter parâmetros a partir de dados sísmicos. Existem 3 etapas principais neste trabalho. Primeiramente, foram estudados o tema da exploração sísmica e a técnica de Algoritmos Genéticos. Na segunda etapa foi definido um modelo, usando Algoritmos Genéticos, que busca, neste caso, minimizar uma medida de erro, para obtenção dos parâmetros objetivos. Finalmente, foi implementado um sistema a partir do modelo proposto e realizados os estudos de casos com dados sísmicos sintéticos para avaliar o seu desempenho. O modelo baseado em Algoritmos Genéticos foi avaliado submetendo-se seus resultados a um especialista e comparando-os com os da busca aleatória. Os resultados obtidos se mostraram consistentemente satisfatórios e sempre superiores aos da busca exaustiva.
This dissertation investigates the use of Genetic Algorithms applied to seismic data with the objective of obtaining physical parameters and seismic attributes that would facilitate the characterization of rocks in terrestrial subsoil. The seismic data has been extensively utilized in the field of petroleum exploration. The applications involving seismic are not restrained to the search for new petroleum reserves, but are also used to project new wells and to improve the production of existing petroleum reservoirs. The survey of seismic data allows the analysis of extended areas of the subsurface at an affordable price relative to other techniques. However, the interpretation of the data with the objective of obtaining relevant and accurate information is not an easy task. For that, several seismic inversion techniques are being developed. This work consists in evaluating an alternative that uses Genetic Algorithms to invert parameters from seismic data. There are 3 main stages in this work. Initially, the theme of seismic exploration and the technique of Genetic Algorithms have been studied. On the second stage a model has been defined, using Genetic Algorithms, which aims, in this case, to minimize an error measurement, obtaining objective parameters. Finally, a system from the proposed model has been implanted and the study of cases with synthetic seismic data has been executed to evaluate its performance. The process of optimizing has been compared to the process of random search and the results obtained by the model have always been superior.
Minichiello, Mark Joseph. « Analysis of genetic variation data using ancestral recombination graphs ». Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613255.
Texte intégralJaffrezic, Florence. « Statistical models for the genetic analysis of longitudinal data ». Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/12274.
Texte intégralAlsulaiman, Thamer. « Detecting complex genetic mutations in large human genome data ». Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6908.
Texte intégralEvenstone, Lauren. « Employing Limited Next Generation Sequence Data for the Development of Genetic Loci of Phylogenetic and Population Genetic Utility ». FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2191.
Texte intégralChen, Li. « Searching for significant feature interaction from biological data ». Diss., Online access via UMI:, 2007.
Trouver le texte intégralRodríguez, Botigué Laura 1984. « Demographic insights of human north African populations using genetic data ». Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/108336.
Texte intégralLa història del Nord d’Àfrica és extremadament complexa, i fins ara ha estat molt difícil determinar a partir de la genètica o l’arqueologia si els primers pobladors van ser reempleçats per migracions posteriors, o si el poblament de la regió ha estat continuat al llarg del temps. Per tal d’investigar els orígens i les migracions de l’home al Nord d’Àfrica he fet servir dos marcadors genètics en un grup de poblacions representatives de la regio, el marcador heretat per via materna, el DNA mitocondrial (mtDNA), i 730,000 SNPs de tot el genoma genotipats amb un xip. He descobert que el Nord d’Àfrica és un mosaic format per un component autòcton amb origens en el Paleolític i un mínim de quatre components més, dos d’ells recents d’origen sub-Saharià i els altres Europeu i d’Orient Proper. També hem descobert un flux genic recent d’origen Nord Africà molt elevat a la Península Ibèrica, i en menor quantitat a Europa.
Lindlöf, Angelica. « Deriving Genetic Networks from Gene Expression Data and Prior Knowledge ». Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-589.
Texte intégralIn this work three different approaches for deriving genetic association networks were tested. The three approaches were Pearson correlation, an algorithm based on the Boolean network approach and prior knowledge. Pearson correlation and the algorithm based on the Boolean network approach derived associations from gene expression data. In the third approach, prior knowledge from a known genetic network of a related organism was used to derive associations for the target organism, by using homolog matching and mapping the known genetic network to the related organism. The results indicate that the Pearson correlation approach gave the best results, but the prior knowledge approach seems to be the one most worth pursuing
Gay, Jo. « Estimating the rate of gene conversion from population genetic data ». Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509942.
Texte intégralHobbs, Mike. « Genetic algorithms for spatial data analysis in geographical information systems ». Thesis, University of Kent, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262636.
Texte intégralSthamer, Harmen-Hinrich. « The automatic generation of software test data using genetic algorithms ». Thesis, University of South Wales, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320726.
Texte intégralXu, Yun. « Chemometrics pattern recognition with applications to genetic and metabolomics data ». Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435733.
Texte intégralKumuthini, Judit. « Extraction of genetic network from microarray data using Bayesian framework ». Thesis, Cranfield University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442547.
Texte intégralEisner, Eric David. « Incorporating diverse data to improve genetic network alignment with IsoRank ». Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/77068.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (p. 26).
To more accurately predict which genes from different species have the same function (orthologs), I extend the network-alignment algorithm IsoRank to simultaneously align multiple unrelated networks over the same set of nodes. In addition to the original protein-interaction networks, I align genetic-interaction networks, gene-expression correlations, and chromosome localization data to improve the functional similarity of aligned genes. Alignments are evaluated with consistency measurements of protein function within ortholog clusters, and with an information-retrieval statistic from a small set of known orthologs. Integrating these additional types of data is shown to improve IsoRank's predictions of classes of genes that have sparse coverage in the original protein-interaction networks.
by Eric David Eisner.
M.Eng.
Gerbault, P. « Modeling demographic and evolutionary history : integrating genetic and archaeological data ». Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1389022/.
Texte intégralTrochet, Holly. « Simple Bayesian approaches to modelling pleiotropy in genetic association data ». Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:a1e3d606-ef39-4ef7-981e-088d6703c04f.
Texte intégralRamachandran, Sohini. « The signature of historical migrations on human population genetic data / ». May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
Texte intégralSalem, Rany Mansour. « Statistical methods for genetic association analysis involving complex longitudinal data ». Diss., [La Jolla] : [San Diego] : University of California, San Diego ; San Diego State University, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3366492.
Texte intégralTitle from first page of PDF file (viewed Aug. 14, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.