Thematische Bibliographien / Genetic data

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Genetic data“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 9. Juni 2025

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Zeitschriftenartikel zum Thema "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 (July 31, 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 data yang memerlukan keamanan ekstra. Blockchain memberikan solusi untuk perlindungan dan pengelolaan data dengan fitur teknologinya yang terdesentralisasi, terenkripsi, setiap transaksi bisa ditelusuri, dan antitampering atau sulit dimodifikasi. Penelitian menerapkan teknologi Blockchain untuk menyimpan dan mengelola data genetik. Sebagai bahan penelitian data genetika manusia diakusisi dari NCBI repository. Data genetik tersebut disimpan dalam Smart contract pada blockchain Ethereum yang ditulis menggunakan bahasa pemrograman Solidity. Setiap transaksi dan penyimpanan data pada Ethereum dibebankan biaya yang cukup mahal atau yang dikenal dengan biaya gas maka penelitian ini menawarkan solusi hanya menyimpan signature saja dari data genetik itu dalam blockchain. Data genetik yang riil dan berukuran besar disimpan dalam InterPlanetary File System (IPFS). Hasil pengujian menjalankan smart contract pada blockchain Ethereum yang hanya menyimpan signature data genetik ini menunjukkan biaya gas yang sangat efisien karena hanya menyimpan 256 bit saja dari data genetik riilnya yang dapat mencapai giga byte. Abstract Human genetics refers to information gathered about the genome or genetic heritage of human individuals. This data includes DNA sequences, genetic variations, mutations, and other information related to individual human genetic traits and characteristics. Human genetic data is obtained through a series of processes, including genetic sequencing, genetic testing, DNA analysis, and genetic mapping. Genetic data, especially in humans, is private data that must be protected by security and confidentiality. Several studies have used Blockchain technology to store data that requires extra security. Blockchain provides solutions for data protection and management with its technological features that are decentralized, encrypted, every transaction can be traced, and anti-tampering or difficult to modify. Research uses Blockchain technology to store and manage genetic data. As research material, human genetic data was acquired from the NCBI repository. The genetic data is stored in Smart contracts on the Ethereum blockchain written using the Solidity programming language. Every transaction and data storage on Ethereum is charged with a fairly expensive fee, known as a gas fee, so this research offers a solution by only storing the signature of the genetic data in the blockchain. The real and large-scale genetic data is stored in the InterPlanetary File System (IPFS). The test results of running a smart contract on the Ethereum blockchain that only stores genetic data signatures show a very efficient gas cost because it only stores 256 bits of real genetic data, which can reach gigabytes.
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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 (June 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 (December 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 (December 1, 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 data sets to perform mega genetic analyses. Having an integrated phenotype database is significant for exploiting the full potential of extensive genotypic data. In this paper, we aim to share our experience gained from integrating four heterogeneous sets of major depression phenotypic data onto the MySQL platform. These data sets constitute clinical data which had been gathered for various genetic studies for the past decade. We also highlight in this report some generic data handling techniques, the costs and benefits regarding the use of integrated phenotype database within our own institution and under the consortium framework.
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Ross-Ibarra, Jeffrey. "Genetic Data Analysis II. Methods for Discrete Population Genentic Data." Economic Botany 56, no. 2 (April 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 (June 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 (March 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 (September 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 (March 1, 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 (June 1, 1991): 248–49. http://dx.doi.org/10.1093/sysbio/40.2.248.

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Dissertationen zum Thema "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. However, new approaches for both the storage and analysis of sequencing data are in imminent needs. In this thesis, we introduce three methods that could be utilized in the management and analysis of sequencing data. In Chapter 1, we propose a novel and simple algorithm for compressing sequencing data that leverages on the scarcity of rare variant data, which enables the storage and analysis of sequencing data efficiently in current hardware environment. We also provide a C++ implementation that supports direct and parallel loading of the compressed format without requiring extra time for decompression. Chapter 2 and 3 focus on the association analysis of sequencing data in population-based design. In Chapter 2, we present a statistical methodology that allows the identification of genetic outliers to obtain a genetically homogeneous subpopulation, which reduces the false positives due to population substructure. Our approach is computationally efficient that can be applied to all the genetic loci in the data and does not require pruning of variants in linkage disequilibrium (LD). In Chapter 3, we propose a general analysis framework in which thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multi-loci analysis, which has focused on the dimension reduction of data, the proposed approach profits from the availability of large numbers of genetic loci. Thus it will be especially relevant for whole-genome sequencing studies which commonly record several thousand loci per gene.
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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 of recombination hotspots that was absent in existing methods. I use samples from the International HapMap Project to obtain recombination rate estimates for the autosomal portion of the genome. Using these estimates, I demonstrate that recombination has a number of interesting relationships with other genome features such as genes, DNA repeats, and sequence motifs. Furthermore, I show that genes of differing function have significantly different rates of recombination. I explore the relationship between recombination and specific sequence motifs and argue that while sequence motifs are an important factor in determining the location of recombination hotspots, the factor that controls motif activity is unknown. The observation of many relationships between recombination and other genome features motivates an attempt to quantify the contributions to the recombination rate from specific features. I employ a wavelet analysis to investigate scale-specific patterns of recombination. In doing so, I reveal a number of highly significant correlations between recombination and other features of the genome at both the fine and broad scales, but find that relatively little of the variation in recombination rates can be explained. I conclude with a discussion of the results contained in the body of the thesis, and suggest a number of areas for future research.
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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 have been advocated about features of genetic architecture (e.g., the contribution of rare vs. common variants). Here, we present a framework which combines multiple empirical datasets and simulation studies to enable systematic testing of hypotheses about both global and locus-specific complex trait architecture. We apply this to type 2 diabetes (T2D).
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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 of alleles along one chromosome, inherited together in a haplotype, it is not always possible to determine with certainty the inheritance pattern, and therefore statistical methods to infer these patterns must be adopted. Due to genotypic heterozygosity, mutliple possible haplotype configurations can often resolve an individual's genotype measures at multiple loci. When haplotypes are not known, but are inferred statistically, an element of uncertainty is thus inherent which, if not dealt with appropriately, can result in unreliable estimates of effect sizes in an association setting. The core aim of the research described in this thesis was to develop and implement a general method for haplotype-based association analysis using multiple imputation to appropriately deal with uncertainty haplotype assignment. Regression-based approaches to association analysis provide flexible methods to investigate the influence of a covariate on a response variable, adjusting for the effects of other variables including interaction terms. ... These methods are then applied to models accommodating binary, quantitative, longitudinal and survival data. The performance of the multiple imputation method implemented was assessed using simulated data under a range of haplotypic effect sizes and genetic inheritance patterns. The multiple imputation approach performed better, on average, than ignoring haplotypic uncertainty, and provided estimates that in most cases were similar to those observed when haplotypes were known. The haplotype association methods developed in this thesis were used to investigate the genetic epidemiology of cardiovascular disease, utilising data for the cholesteryl ester transfer protein gene (CETP), the hepatic lipase (LIPC) gene and the 15- lipoxygenase (ALOX15) gene on a total of 6,487 individuals from three Western Australian studies. Results of these analyses suggested single nucleotide polymorphisms (SNPs) and haplotypes in the CETP gene were associated with increased plasma high-density lipoprotein cholesterol (HDL-C). SNPs in the LIPC gene were also associated with increased HDL-C and haplotypes in the ALOX15 gene were associated with risk of carotid plaque among individuals with premature CHD. The research presented in this thesis is both novel and important as it provides methods for the analysis of haplotypic associations with a range of response types, while incorporating information about haplotype uncertainty inherent in populationbased studies. These methods are shown to perform well for a range of simulated and real data situations, and have been written into a statistical analysis package that has been freely released to the research community.
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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 programming (GP) is one of the effective evolutionary computation techniques to solve classification problems. GP solves classification problems as an optimization tasks, where it searches for the best solution with highest accuracy. However, GP suffers from some weaknesses such as long execution time, and the need to tune many parameters for each problem. Furthermore, GP can not obtain high accuracy for multiclass classification problems as opposed to binary problems. In this dissertation, we address these drawbacks and propose some approaches in order to overcome them. Adaptive GP variants are proposed in order to automatically adapt the parameter settings and shorten the execution time. Moreover, two approaches are proposed to improve the accuracy of GP when applied to multiclass classification problems. In addition, a Segment-based approach is proposed to accelerate the GP execution time for the data classification problem. Furthermore, a parallelization of the GP process using the MapReduce methodology was proposed which aims to shorten the GP execution time and to provide the ability to use large population sizes leading to a faster convergence. The proposed approaches are evaluated using different measures, such as accuracy, execution time, sensitivity, specificity, and statistical tests. Comparisons between the proposed approaches with the standard GP, and with other classification techniques were performed, and the results showed that these approaches overcome the drawbacks of standard GP by successfully improving the accuracy and execution time.
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Bücher zum Thema "Genetic data"

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

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Langdon, W. B. Genetic Programming and Data Structures. Boston, MA: 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. Bethesda, Md: The Institutes, 1987.

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

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

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

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Buchteile zum Thema "Genetic data"

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Cippitani, Roberto. "Genetic Data." In GDPR Requirements for Biobanking Activities Across Europe, 227–32. Cham: 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, 75–91. New York: 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, 1. Boston, MA: 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, 568. Boston, MA: 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, 13–38. Totowa, NJ: 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, 55–86. London: 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, 141–63. Cham: 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, 43–59. Boston, MA: 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, 51–70. Berlin, Heidelberg: 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, 2–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34156-4_2.

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Konferenzberichte zum Thema "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), 395. 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, 133–40. 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 transparency and trust in customer relationships. The proposed integration involves redefining the CRM Vision to prioritize hy­per-personalization, adapting core CRM activities to accommodate genet­ic data, and emphasizing robust data privacy measures. This research aims to inform businesses about the transformative potential of genetic insights in CRM processes and the importance of ethical and compliant practices.
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Wineberg, Mark, and Sebastian Lenartowicz. "Reexpressing problematic optimization data." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: 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. New York, New York, USA: 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. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/1741906.1742162.

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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. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389930.

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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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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 presented outlines the integration of genetic data into differ­ent stages of sustainable product development thus allowing for precision targeting through responsible innovation management. The paper also ex­amines the potential ethical and legal implications of using genetic data in marketing management.
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Berichte der Organisationen zum Thema "Genetic data"

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), February 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), June 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. Cambridge, MA: National Bureau of Economic Research, August 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, May 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, May 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, December 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 to be used to generate probes for establishing high-throughput alternative transcriptome analysis in the form of a splicing-specific oligonucleotide microarray. We achieved the first goal and established a database and web site termed Alternative Splicing In Plants (ASIP, http://www.plantgdb.org/ASIP/). We also thoroughly reviewed the extent of alternative splicing in plants (Arabidopsis and rice) and proposed mechanisms for transcript processing. We noted that the repertoire of plant alternative splicing differs from that encountered in animals. For example, intron retention turned out to be the major type. This surprising development was proven by direct RNA isolation techniques. We further analyzed EST databases available from many plants and developed a process to assess their alternative splicing rate. Our results show that the lager genome-sized plant species have enhanced rates of alternative splicing. We did advance gene prediction accuracy in plants by incorporating scoring for non-canonical introns. Our data and programs are now being used in the continuing annotation of plant genomes of agronomic importance, including corn, soybean, and tomato. Based on the gene annotation data developed in the early part of the project, it turned out that specific probes for different exons could not be scaled up to a large array because no uniform hybridization conditions could be found. Therefore, we modified our original objective to design and produce an oligonucleotide microarray for probing alternative splicing and realized that it may be reasonable to investigate the extent of alternative splicing using novel commercial whole genome arrays. This possibility was directly examined by establishing algorithms for the analysis of such arrays. The predictive value of the algorithms was then shown by isolation and verification of alternative splicing predictions from the published whole genome array databases. The BARD-funded work provides a significant advance in understanding the extent and possible roles of alternative splicing in plants as well as a foundation for advances in computational gene prediction.
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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), January 2024. http://dx.doi.org/10.2172/2335448.

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Oyler-McCance, Sara, Lee Jones, Blake McCann, Brendan Moynahan, Paul Santavy, Kathryn Schoenecker, Shawna Zimmerman, 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 at or below carrying capacity. The loss of genetic diversity within bison, and the fact that most DOI herds are relatively small and isolated from each other with no opportunity for natural gene flow, raises concerns about maintaining genetic diversity over the long term. Connecting populations through gene flow (i.e., creating a metapopulation) can minimize loss of genetic diversity, both within and across populations. Management of DOI bison conservation herds has historically varied across bureaus and conservation units. Adopting a national perspective on bison conservation was identified as a priority in the 2008 Department of the Interior Bison Conservation Initiative (BCI). The concept of metapopulation management as a potential tool to maximize the conservation of genetic diversity among DOI herds was first described in this 2008 Initiative and was specifically encouraged in the 2010 DOI Bison Conservation Genetics Workshop report (Dratch and Gogan 2010). In the 2020 BCI, the DOI re-affirmed its commitment to conserving bison as native, North American wildlife. This document establishes a framework for a nationally coordinated strategy for bison managed by the DOI to support the genetic conservation goals outlined in the 2020 BCI. This is a decision-making framework that guides managers through the process of determining when and how to consider translocations. Decisions and actions within the framework are informed by analysis and interpretation of data housed in an integrated, relational database that will be initially populated with the most current data and updated annually thereafter. It provides science-based guidance on how to conserve DOI bison genetic diversity through strategic translocations, while also considering cattle introgression and bison health. We illustrate how this Strategy can be used to guide the establishment of new conservation herds and discuss what it means to be a DOI partner. Finally, this is intended to be used as a living document that will evolve as needs and technologies change.
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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-in donation system of health data for research purposes. In this article, we argue that PMDD is not a useful model for addressing the issue at hand, as it does not consider that some of these data (the genetic data) may be the personal data of the living relatives of the deceased. Furthermore, we find the reasons supporting an opt-in model less convincing than those that vouch for alternative systems. Indeed, we propose a normative framework that is based on the opt-out system for non-personal data combined with the application of the GDPR to the relatives’ personal data.
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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, January 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 sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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