Bibliografías temáticas / Genetic data

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Literatura académica sobre el tema "Genetic data"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Genetic data"

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Famuji, Tri Stiyo, Herman Herman, and Sunardi Sunardi. "Smart Contract Penyimpanan Data Genetika Manusia Berbiaya Murah pada Blockchain Ethereum." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 3 (2024): 695–704. http://dx.doi.org/10.25126/jtiik.1137558.

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Genetika manusia merujuk pada informasi yang dikumpulkan tentang genom atau warisan genetik individu manusia. Data ini mencakup sekuens DNA, variasi genetik, mutasi, dan informasi lain yang terkait dengan sifat dan karakteristik genetik individu manusia. Data genetika manusia diperoleh melalui serangkaian proses, meliputi penguntaian genetik, pengujian genetik, analisis DNA, dan pemetaan genetik. Data genetika terutama pada manusia merupakan data yang bersifat privat yang harus dilindungi keamanan dan kerahasiaanya. Beberapa penelitian telah menggunakan teknologi Blockchain untuk menyimpan dat
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Volkova, T., E. Furta, O. Dmitrieva, and I. Shabalina. "Pattern Building Methods in Genetic Data Processing." Journal on Selected Topics in Nano Electronics and Computing 1, no. 2 (2014): 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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Taylor, Mark J. "Data Protection, Shared (Genetic) Data and Genetic Discrimination." Medical Law International 8, no. 1 (2006): 51–77. http://dx.doi.org/10.1177/096853320600800103.

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Butler, Amy W., Sarah Cohen-Woods, Anne Farmer, Peter McGuffin, and Cathryn M. Lewis. "Integrating Phenotypic Data For Depression." Journal of Integrative Bioinformatics 7, no. 3 (2010): 290–99. http://dx.doi.org/10.1515/jib-2010-136.

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Abstract The golden era of molecular genetic research brings about an explosion of phenotypic, genotypic and sequencing data. Building on the common aims to exploit understanding of human diseases, it also opens up an opportunity for scientific communities to share and combine research data. Genome-wide association studies (GWAS) have been widely used to locate genetic variants, which are susceptible for common diseases. In the field of medical genetics, many international collaborative consortiums have been established to conduct meta-analyses of GWAS results and to combine large genotypic da
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Ross-Ibarra, Jeffrey. "Genetic Data Analysis II. Methods for Discrete Population Genentic Data." Economic Botany 56, no. 2 (2002): 216. http://dx.doi.org/10.1663/0013-0001(2002)056[0216:gdaimf]2.0.co;2.

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Slatkin, Montgomery, Wayne P. Maddison, and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Zoology 40, no. 2 (1991): 248. http://dx.doi.org/10.2307/2992265.

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Chase, Gary A., and Bruce S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Journal of the American Statistical Association 86, no. 413 (1991): 248. http://dx.doi.org/10.2307/2289745.

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Feytmans, E., and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Biometrics 47, no. 3 (1991): 1205. http://dx.doi.org/10.2307/2532683.

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Morton, N. E. "Genetic Data Analysis. Methods for Discrete Population Genetic Data." Journal of Medical Genetics 29, no. 3 (1992): 216. http://dx.doi.org/10.1136/jmg.29.3.216.

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Slatkin, M., and W. P. Maddison. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Biology 40, no. 2 (1991): 248–49. http://dx.doi.org/10.1093/sysbio/40.2.248.

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Tesis sobre el tema "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.

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During the last two decades, genotyping technology has advanced rapidly, which enabled the tremendous success of genome-wide association studies (GWAS) in the search of disease susceptibility loci (DSLs). However, only a small fraction of the overall predicted heritability can be explained by the DSLs discovered. One possible explanation for this ”missing heritability” phenomenon is that many causal variants are rare. The recent development of high-throughput next-generation sequencing (NGS) technology provides the instrument to look closely at these rare variants with precision and efficiency
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Haroun, 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.

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Lankhorst, Marc Martijn. "Genetic algorithms in data analysis." [S.l. : [Groningen] : s.n.] ; [University Library Groningen] [Host], 1996. http://irs.ub.rug.nl/ppn/142964662.

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Hiden, 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.

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Auton, 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.

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Genetic recombination is an important process that generates new combinations of genes on which natural selection can operate. As such, an understanding of recombination in the human genome will provide insight into the evolutionary processes that have shaped our genetic history. The aim of this thesis is to use samples of population genetic data to explore the patterns of variation in the rate of recombination in the human genome. To do this I introduce a novel means of estimating recombination rates from population genetic data. The new, computationally efficient method incorporates a model
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Agarwala, 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.

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Most common diseases have substantial heritable components but are characterized by complex inheritance patterns implicating numerous genetic and environmental factors. A longstanding goal of human genetics research is to delineate the genetic architecture of these traits - the number, frequencies, and effect sizes of disease-causing alleles - to inform mapping studies, elucidate mechanisms of disease, and guide development of targeted clinical therapies and diagnostics. Although vast empirical genetic data has now been collected for common diseases, different and contradictory hypotheses ha
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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/.

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Li, 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.

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Thesis(Ph.D.)--Case Western Reserve University, 2010<br>Title 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
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McCaskie, 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.

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[Truncated abstract] This thesis investigates novel methods for the genetic association analysis of haplotype data in samples of unrelated individuals, and applies these methods to the analysis of coronary heart disease and related phenotypes. Determining the inheritance pattern of genetic variants in studies of unrelated individuals can be problematic because family members of the studied individuals are often not available. For the analysis of individual genetic loci, no problem arises because the unit of interest is the observed genotype. When the unit of interest is the linear combination
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Al-Madi, Naila Shikri. "Improved Genetic Programming Techniques For Data Classification." Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27097.

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Evolutionary algorithms are one category of optimization techniques that are inspired by processes of biological evolution. Evolutionary computation is applied to many domains and one of the most important is data mining. Data mining is a relatively broad field that deals with the automatic knowledge discovery from databases and it is one of the most developed fields in the area of artificial intelligence. Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programmin
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Libros sobre el tema "Genetic data"

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Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sinauer Associates, 1996.

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Langdon, W. B. Genetic Programming and Data Structures. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5731-9.

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1936-, Anderson W. French, and National Institutes of Health (U.S.). Recombinant DNA Advisory Committee. Human Gene Therapy Subcommittee, eds. Human gene therapy: Preclinical data document. The Institutes, 1987.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02830-8.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-07418-3.

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Lin, Shili, and Hongyu Zhao. Handbook on Analyzing Human Genetic Data. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-69264-5.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures = Evolution Programs. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03315-9.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures =: Evolution Programs. Springer Berlin Heidelberg, 1994.

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Lin, Shili. Handbook on Analyzing Human Genetic Data: Computational Approaches and Software. Springer-Verlag Berlin Heidelberg, 2010.

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EuroGP 2007 (2007 Valencia, Spain). Genetic programming: Proceedings. Springer, 2007.

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Capítulos de libros sobre el tema "Genetic data"

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Cippitani, Roberto. "Genetic Data." In GDPR Requirements for Biobanking Activities Across Europe. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42944-6_25.

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Agbinya, Johnson I. "Genetic Algorithm." In Applied Data Analytics - Principles and Applications. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337225-5.

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Sipper, Moshe. "Genetic Programming." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7502-7_376-1.

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Sipper, Moshe. "Genetic Programming." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_376.

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Eisen, Jonathan A. "The Genetic Data Environment." In Sequence Data Analysis Guidebook. Humana Press, 1997. http://dx.doi.org/10.1385/0-89603-358-9:13.

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Yin, Yong, Ikou Kaku, Jiafu Tang, and JianMing Zhu. "Genetic Algorithm-based Fuzzy Nonlinear Programming." In Data Mining. Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-338-1_4.

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Isik, Fikret, James Holland, and Christian Maltecca. "Genetic Values." In Genetic Data Analysis for Plant and Animal Breeding. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55177-7_5.

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Langdon, W. B. "Advanced Genetic Programming Techniques." In Genetic Programming and Data Structures. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5731-9_3.

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Maulik, Ujjwal, Sanghamitra Bandyopadhyay, and Anirban Mukhopadhyay. "Data Mining Fundamentals." In Multiobjective Genetic Algorithms for Clustering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16615-0_3.

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Sebastiani, Paola. "Intelligent Data Analysis of Human Genetic Data." In Advances in Intelligent Data Analysis XI. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34156-4_2.

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Actas de conferencias sobre el tema "Genetic data"

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Pinho, Armando J., and Diogo Pratas. "Optimization of Data Compression Parameters Using Genetic Algorithms." In 2025 Data Compression Conference (DCC). IEEE, 2025. https://doi.org/10.1109/dcc62719.2025.00082.

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Ivanova-Kadiri, Ivelina. "Transforming the CRM Diamond Model with Genetic Data Integration." In 8th International Scientific Conference – EMAN 2024 – Economics and Management: How to Cope With Disrupted Times. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2024. https://doi.org/10.31410/eman.s.p.2024.133.

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This paper explores the integration of genetic data into the CRM Diamond model, proposing a new model for CRM-Diamond with the incor­poration of customer genetic data. It offers insights into the implications for customer relationship management (CRM), emphasizing enhanced cus­tomer segmentation, personalized marketing strategies, and improved en­gagement. However, this integration presents challenges related to data pri­vacy, ethical considerations, and regulatory compliance. The study exam­ines these challenges and proposes strategies for responsible implementa­tion while ensuring transparen
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Wineberg, Mark, and Sebastian Lenartowicz. "Reexpressing problematic optimization data." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3071178.3079190.

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Eggermont, Jeroen, Joost N. Kok, and Walter A. Kosters. "Genetic Programming for data classification." In the 2004 ACM symposium. ACM Press, 2004. http://dx.doi.org/10.1145/967900.968104.

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Hong, Tzung-Pei, Chun-Hao Chen, and Vincent S. Tseng. "Genetic-Fuzzy Data Mining Techniques." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.157.

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Mansour, Nashat, Rouba Zantout, and Mirvat El-Sibai. "Mining breast cancer genetic data." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818131.

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Kurveyv, Mamta, D. K. Chitre, and Hemlata Patil. "Genetic algorithm for data mining." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. ACM, 2010. http://dx.doi.org/10.1145/1741906.1742162.

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Kumar, T. V. Vijay, Vikram Singh, and Ajay Kumar Verma. "Generating Distributed Query Processing Plans Using Genetic Algorithm." In 2010 International Conference on Data Storage and Data Engineering (DSDE). IEEE, 2010. http://dx.doi.org/10.1109/dsde.2010.56.

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Attaoui, Mohammed Oualid, Hanene Azzag, Mustapha Lebbah, and Nabil Keskes. "Multi-objective data stream clustering." In GECCO '20: Genetic and Evolutionary Computation Conference. ACM, 2020. http://dx.doi.org/10.1145/3377929.3389930.

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Ivanova-Kadiri, Ivelina. "Customer Genetic Data for Sustainability and Innovation Management." In 9th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2023. http://dx.doi.org/10.31410/eraz.s.p.2023.169.

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The availability of affordable genetic testing has enabled the col­lection of vast amounts of genetic data, creating new opportunities for mar­keting management. The use of genetic data empowers companies to de­velop personalized products and services and enhance customer relation­ship management. This, in turn, creates a competitive advantage for boost­ing companies’ strategic market positioning by enhancing their sustainabil­ity and innovation policies. This review paper aims to explore how business­es can leverage genetic data for sustainability and innovation management. The framework pres
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Informes sobre el tema "Genetic data"

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1419729.

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1441274.

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Cawley, John, Euna Han, Jiyoon (June) Kim, and Edward Norton. Testing for Peer Effects Using Genetic Data. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23719.

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Sharma, Prakriti, and Anne Fennell. Dataset : Ampleography data for Marquette grafted on an interspecific rootstock population. South Dakota State University, 2025. https://doi.org/10.62812/ebzx2792.

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Sharma, Prakriti, and Anne Fennell. Dataset : Twenty-one amplographic landmark data for interspecific F1 (V. rupestrisx V. riparia) rootstock population. South Dakota State University, 2025. https://doi.org/10.62812/twth2085.

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Fluhr, Robert, and Volker Brendel. Harnessing the genetic diversity engendered by alternative gene splicing. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696517.bard.

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Our original objectives were to assess the unexplored dimension of alternative splicing as a source of genetic variation. In particular, we sought to initially establish an alternative splicing database for Arabidopsis, the only plant for which a near-complete genome has been assembled. Our goal was to then use the database, in part, to advance plant gene prediction programs that are currently a limiting factor in annotating genomic sequence data and thus will facilitate the exploitation of the ever increasing quantity of raw genomic data accumulating for plants. Additionally, the database was
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Michor, Madis, Mark Schlossman, and Alexander Heifetz. Progress in Genetic Algorithm Fitting of X-Ray Fluorescence Data for Absorption Spectroscopy of Liquid Surfaces. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2335448.

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Oyler-McCance, Sara, Lee Jones, Blake McCann, et al. A metapopulation strategy to support long term conservation of genetic diversity in Department of the Interior bison. National Park Service, 2024. https://doi.org/10.36967/2307352.

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Once numbering in the tens of millions, plains bison (Bison bison bison) were nearly driven to extinction with only a few hundred individuals remaining by the late 19th century. Plains bison have since recovered to approximately 20,000 animals managed in conservation herds throughout North America, yet substantial challenges to their recovery remain. The Department of the Interior (DOI) is working with diverse partners to steward approximately 11,000 bison in 18 conservation herds across 12 states. Most herds exist in areas without native predators, and removals are required to keep herd sizes
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de Miguel Beriain, Iñigo, Aliuska Duardo Sánchez, and José Antonio Castillo Parrilla. What Can We Do with the Data of Deceased People? A Normative Proposal. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64580.

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The health and genetic data of deceased people are a particularly important asset in the field of biomedical research. However, in practice, using them is compli- cated, as the legal framework that should regulate their use has not been fully developed yet. The General Data Protection Regulation (GDPR) is not applicable to such data and the Member States have not been able to agree on an alternative regulation. Recently, normative models have been proposed in an attempt to face this issue. The most well- known of these is posthumous medical data donation (PMDD). This proposal supports an opt-i
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule se
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