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Journal articles on the topic 'GENETIC FRAMEWORK'

Author: Grafiati

Published: 11 September 2023

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1

De Meester, Luc, Joost Vanoverbeke, Koen De Gelas, Raquel Ortells, and Piet Spaak. "Genetic structure of cyclic parthenogenetic zooplankton populations – a conceptual framework." Archiv für Hydrobiologie 167, no. 1-4 (October 5, 2006): 217–44. http://dx.doi.org/10.1127/0003-9136/2006/0167-0217.

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2

Venkatraman, S., and G. G. Yen. "A Generic Framework for Constrained Optimization Using Genetic Algorithms." IEEE Transactions on Evolutionary Computation 9, no. 4 (August 2005): 424–35. http://dx.doi.org/10.1109/tevc.2005.846817.

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3

Godfrey, K. "Genetic databank launches ethics framework." BMJ 327, no. 7417 (September 27, 2003): 700–0. http://dx.doi.org/10.1136/bmj.327.7417.700.

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4

Currens, Kenneth P., and Craig A. Busack. "A Framework for Assessing Genetic Vulnerability." Fisheries 20, no. 12 (December 1995): 24–31. http://dx.doi.org/10.1577/1548-8446(1995)020<0024:affagv>2.0.co;2.

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5

Farashi, Sajjad, and Mohammad Mikaili. "Genetic Algorithm Framework for Spike Sorting." International Journal of Image, Graphics and Signal Processing 7, no. 4 (March 8, 2015): 42–50. http://dx.doi.org/10.5815/ijigsp.2015.04.05.

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6

Parcy, François, Ove Nilsson, Maximilian A. Busch, Ilha Lee, and Detlef Weigel. "A genetic framework for floral patterning." Nature 395, no. 6702 (October 1998): 561–66. http://dx.doi.org/10.1038/26903.

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7

Terry, Sharon F. "An Evidence Framework for Genetic Testing." Genetic Testing and Molecular Biomarkers 21, no. 7 (July 2017): 407–8. http://dx.doi.org/10.1089/gtmb.2017.29032.sjt.

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8

Vaidyanathan, Prashant, Bryan S. Der, Swapnil Bhatia, Nicholas Roehner, Ryan Silva, Christopher A. Voigt, and Douglas Densmore. "A Framework for Genetic Logic Synthesis." Proceedings of the IEEE 103, no. 11 (November 2015): 2196–207. http://dx.doi.org/10.1109/jproc.2015.2443832.

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9

Rodriguez-Villalon, A., B. Gujas, Y. H. Kang, A. S. Breda, P. Cattaneo, S. Depuydt, and C. S. Hardtke. "Molecular genetic framework for protophloem formation." Proceedings of the National Academy of Sciences 111, no. 31 (July 21, 2014): 11551–56. http://dx.doi.org/10.1073/pnas.1407337111.

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10

Twigg, Stephen R. F., and Andrew O. M. Wilkie. "A Genetic-Pathophysiological Framework for Craniosynostosis." American Journal of Human Genetics 97, no. 3 (September 2015): 359–77. http://dx.doi.org/10.1016/j.ajhg.2015.07.006.

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11

McDonald, B. A., and C. Linde. "Disease resistance and pathogen population genetic." Plant Protection Science 38, SI 1 - 6th Conf EFPP 2002 (January 1, 2002): 245–48. http://dx.doi.org/10.17221/10375-pps.

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Plant pathologists have seen many boom-and-bust cycles following the deployment of resistant varieties. These cycles result when pathogen populations adapt to the presence of a major resistance gene by evolving a new population that can overcome this resistance gene. The breakdown of genetic resistance is due to the evolution of the local pathogen population because of selection for mutants, recombinants, or immigrants that are better adapted to the resistant cultivar. To understand the process that leads to breakdown of a resistance gene, we need to understand the processes that govern pathogen evolution. Population geneticists have identified five evolutionary forces that interact to affect the evolution of organisms. We ranked these risks and developed a quantitative framework to predict the risk that a pathogen will evolve to overcome major resistance genes. Our hypothesis is that much of the durability of resistance genes is due to the nature of the pathogen population rather than to the nature of the resistance gene. The framework we developed can be used as a hypothesis to test against a large number of plant pathosystems. The underlying principles of the framework can be tested individually or in combination according to the available knowledge of the population genetics for any pathogen. We propose that this framework can be used to design breeding strategies to break the boom-and-bust cycle and lead to durable resistance.

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12

Mayor, S. "Human Genetics Commission develops framework for direct to consumer genetic tests." BMJ 338, may15 2 (May 15, 2009): b1995. http://dx.doi.org/10.1136/bmj.b1995.

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13

Fan, Weiguo, Michael D. Gordon, and Praveen Pathak. "A generic ranking function discovery framework by genetic programming for information retrieval." Information Processing & Management 40, no. 4 (July 2004): 587–602. http://dx.doi.org/10.1016/j.ipm.2003.08.001.

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14

Yao, Jian, Weiping Wang, Zhifei Li, Yonglin Lei, and Qun Li. "Tactics Exploration Framework based on Genetic Programming." International Journal of Computational Intelligence Systems 10, no. 1 (2017): 804. http://dx.doi.org/10.2991/ijcis.2017.10.1.53.

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15

Muto, T., R. J. Steel, and J. B. Swenson. "Autostratigraphy: A Framework Norm for Genetic Stratigraphy." Journal of Sedimentary Research 77, no. 1 (January 1, 2007): 2–12. http://dx.doi.org/10.2110/jsr.2007.005.

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16

Anderson, Susan, Walter Sadinski, Lee Shugart, Peter Brussard, Michael Depledge, Tim Ford, JoEllen Hose, et al. "Genetic and Molecular Ecotoxicology: A Research Framework." Environmental Health Perspectives 102, suppl 12 (December 1994): 3–8. http://dx.doi.org/10.1289/ehp.94102s123.

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17

Southgate, Laura, Rajiv D. Machado, Stefan Gräf, and Nicholas W. Morrell. "Molecular genetic framework underlying pulmonary arterial hypertension." Nature Reviews Cardiology 17, no. 2 (August 12, 2019): 85–95. http://dx.doi.org/10.1038/s41569-019-0242-x.

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18

Knollmann, Björn C., and Dan M. Roden. "A genetic framework for improving arrhythmia therapy." Nature 451, no. 7181 (February 2008): 929–36. http://dx.doi.org/10.1038/nature06799.

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19

Rudnick, E. M., J. H. Patel, G. S. Greenstein, and T. M. Niermann. "A genetic algorithm framework for test generation." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 16, no. 9 (1997): 1034–44. http://dx.doi.org/10.1109/43.658571.

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20

Busch, R. M., and R. R. West. "Hierarchal genetic stratigraphy: A framework for paleoceanography." Paleoceanography 2, no. 2 (April 1987): 141–64. http://dx.doi.org/10.1029/pa002i002p00141.

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21

Bondar', Irina Arkad'evna, and Olesya Yur'evna Shabel'nikova. "Genetic framework of type 2 diabetes mellitus." Diabetes mellitus 16, no. 4 (December 18, 2013): 11–16. http://dx.doi.org/10.14341/dm2013411-16.

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More than 100 genes associated with the risk of type 2 diabetes mellitus (T2DM) are now established. Most of them affect insulin secretion, adipogenesis and insulin resistance, but the exact molecular mechanisms determining their involvement in the pathogenesis of T2DM are not understood completely.

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22

Waldo, A. L. "A genetic framework for improving arrhythmia therapy." Yearbook of Cardiology 2009 (January 2009): 477. http://dx.doi.org/10.1016/s0145-4145(09)79599-1.

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23

Tanev, I. T. "T2-4 XML-Based Genetic Programming Framework." Proceedings of The Computational Mechanics Conference 2007.20 (2007): 58–59. http://dx.doi.org/10.1299/jsmecmd.2007.20.58.

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24

Vasudevan, Vinaya, Patrick Samson, Arthur D. Smith, and Zeph Okeke. "The genetic framework for development of nephrolithiasis." Asian Journal of Urology 4, no. 1 (January 2017): 18–26. http://dx.doi.org/10.1016/j.ajur.2016.11.003.

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25

Young, Mary-Anne, Kate Thompson, Jeremy Lewin, and Lucy Holland. "A framework for youth-friendly genetic counseling." Journal of Community Genetics 11, no. 2 (November 5, 2019): 161–70. http://dx.doi.org/10.1007/s12687-019-00439-2.

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26

Riaz, S., R. Hu, and M. A. Walker. "A framework genetic map of Muscadinia rotundifolia." Theoretical and Applied Genetics 125, no. 6 (June 12, 2012): 1195–210. http://dx.doi.org/10.1007/s00122-012-1906-7.

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27

Kant, Tapan, Manjari Gupta, Anil Kumar Tripathi, and Meeta Prakash. "Detecting Meta-Patterns from Frameworks Using Hybrid Genetic Algorithm." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 350. http://dx.doi.org/10.14419/ijet.v7i2.20.16732.

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Meta-patterns are a sort of basic object-oriented constructs that have been used to design an object-oriented framework. It has been used to precisely describe possible design pattern of a framework at meta-level to manifest framework hot-spots and its corresponding adaptability. The present study is an attempt to develop a genetic algorithm approach for detecting the types and numbers of meta-patterns. For this purpose we have converted the UML class diagram of object-oriented framework and meta-patterns into directed graph and applied hybrid genetic algorithm. The obtained results from the proposed algorithm are further validated manually with the recall and precision percentage of 86.20 and 80.64 respectively. Overall the study demonstrates the utility of the uniquely proposed algorithm for the near accurate identification of meta-patterns for high reusability. This can be applied on frameworks for assessing the evolution process, documentation of hot-spots and reducing the customization effort.

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28

Koromina, Maria, Vasileios Fanaras, Gareth Baynam, Christina Mitropoulou, and George P. Patrinos. "Ethics and equity in rare disease research and healthcare." Personalized Medicine 18, no. 4 (July 2021): 407–16. http://dx.doi.org/10.2217/pme-2020-0144.

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Rapid advances in next-generation sequencing technology, particularly whole exome sequencing and whole genome sequencing, have greatly affected our understanding of genetic variation underlying rare genetic diseases. Herein, we describe ethical principles of guiding consent and sharing of genomics research data. We also discuss ethical dilemmas in rare diseases research and patient recruitment policies and address bioethical and societal aspects influencing the ethical framework for genetic testing. Moreover, we focus on addressing ethical issues surrounding research in low- and middle-income countries. Overall, this perspective aims to address key aspects and issues for building proper ethical frameworks, when conducting research involving genomics data with a particular emphasis on rare diseases and genetics testing.

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29

Harismendy, Olivier, Jihoon Kim, Xiaojun Xu, and Lucila Ohno-Machado. "Evaluating and sharing global genetic ancestry in biomedical datasets." Journal of the American Medical Informatics Association 26, no. 5 (March 14, 2019): 457–61. http://dx.doi.org/10.1093/jamia/ocy194.

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Abstract Genetic ancestry is a critical co-factor to study phenotype-genotype associations using cohorts of human subjects. Most publicly available molecular datasets are, however, missing this information or only share self-reported race and ethnicity, representing a limitation to identify and repurpose datasets to investigate the contribution of ancestry to diseases and traits. We propose an analytical framework to enrich the metadata from publicly available cohorts with genetic ancestry information and a resulting diversity score at continental resolution, calculated directly from the data. We illustrate this framework using The Cancer Genome Atlas datasets searched through the DataMed Data Discovery Index. Data repositories and contributors can use this framework to provide genetic diversity measurements for controlled access datasets, minimizing the work involved in requesting a dataset that may ultimately prove inadequate for a researcher’s purpose. With the increasing global scale of human genetics research, studies on disease risk and susceptibility would benefit greatly from the adequate estimation and sharing of genetic diversity in publicly available datasets following a framework such as the one presented.

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30

Mowlaei, Mohammad Erfan, and Xinghua Shi. "FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms." Genes 14, no. 5 (May 9, 2023): 1059. http://dx.doi.org/10.3390/genes14051059.

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(1) Background: Phenotype prediction is a pivotal task in genetics in order to identify how genetic factors contribute to phenotypic differences. This field has seen extensive research, with numerous methods proposed for predicting phenotypes. Nevertheless, the intricate relationship between genotypes and complex phenotypes, including common diseases, has resulted in an ongoing challenge to accurately decipher the genetic contribution. (2) Results: In this study, we propose a novel feature selection framework for phenotype prediction utilizing a genetic algorithm (FSF-GA) that effectively reduces the feature space to identify genotypes contributing to phenotype prediction. We provide a comprehensive vignette of our method and conduct extensive experiments using a widely used yeast dataset. (3) Conclusions: Our experimental results show that our proposed FSF-GA method delivers comparable phenotype prediction performance as compared to baseline methods, while providing features selected for predicting phenotypes. These selected feature sets can be used to interpret the underlying genetic architecture that contributes to phenotypic variation.

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31

Lara, Letícia A. de C., Ivan Pocrnic, Thiago de P. Oliveira, R. Chris Gaynor, and Gregor Gorjanc. "Temporal and genomic analysis of additive genetic variance in breeding programmes." Heredity 128, no. 1 (December 15, 2021): 21–32. http://dx.doi.org/10.1038/s41437-021-00485-y.

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AbstractGenetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium.

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32

Penke, Lars, Jaap J. A. Denissen, and Geoffrey F. Miller. "The evolutionary genetics of personality." European Journal of Personality 21, no. 5 (August 2007): 549–87. http://dx.doi.org/10.1002/per.629.

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Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics. We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation‐selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation‐selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model. This evolutionary genetic framework highlights the role of gene‐environment interactions in the study of personality, yields new insight into the person‐situation‐debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality. Copyright © 2007 John Wiley & Sons, Ltd.

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33

Wiens, Miriam E., Brenda J. Wilson, Christina Honeywell, and Holly Etchegary. "A family genetic risk communication framework: guiding tool development in genetics health services." Journal of Community Genetics 4, no. 2 (January 15, 2013): 233–42. http://dx.doi.org/10.1007/s12687-012-0134-9.

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34

Sankara Narayanan, K. "Tolkappiyam Masculine Creatures in the Sangam Literature Framework." Shanlax International Journal of Tamil Research 4, no. 4 (April 1, 2020): 11–18. http://dx.doi.org/10.34293/tamil.v4i4.2313.

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Tholkappiayam is the oldest grammar of Tamil. It is divied into three chapters: Ezhuthathigaram, Cholathigaram, Porulathigaram Thokappiya Porulathigaram discriminates in three genetics. They are as follows 1, Youthful names, 2. Masculine names, 3. Feminine names. The three objectives of this review article are as follows, Mathching the names of masculine Species pointing of the Tholkappiyam genetic in the Sangam Literature. Examples are the masculine names found in the Sangam Literature. Outline the changes and developments in masculine names.

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35

VERGILIO, SILVIA REGINA, and AURORA POZO. "A GRAMMAR-GUIDED GENETIC PROGRAMMING FRAMEWORK CONFIGURED FOR DATA MINING AND SOFTWARE TESTING." International Journal of Software Engineering and Knowledge Engineering 16, no. 02 (April 2006): 245–67. http://dx.doi.org/10.1142/s0218194006002781.

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Genetic Programming (GP) is a powerful software induction technique that can be applied to solve a wide variety of problems. However, most researchers develop tailor-made GP tools for solving specific problems. These tools generally require significant modifications in their kernel to be adapted to other domains. In this paper, we explore the Grammar-Guided Genetic Programming (GGGP) approach as an alternative to overcome such limitation. We describe a GGGP based framework, named Chameleon, that can be easily configured to solve different problems. We explore the use of Chameleon in two domains, not usually addressed by works in the literature: in the task of mining relational databases and in the software testing activity. The presented results point out that the use of the grammar-guided approach helps us to obtain more generic GP frameworks and that they can contribute in the explored domains.

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36

Hu, Jianjun, Erik Goodman, Kisung Seo, Zhun Fan, and Rondal Rosenberg. "The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms." Evolutionary Computation 13, no. 2 (June 2005): 241–77. http://dx.doi.org/10.1162/1063656054088530.

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Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.

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37

Werme, Josefin, Sophie van der Sluis, Danielle Posthuma, and Christiaan A. de Leeuw. "An integrated framework for local genetic correlation analysis." Nature Genetics 54, no. 3 (March 2022): 274–82. http://dx.doi.org/10.1038/s41588-022-01017-y.

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38

Nafar, Enas, and Said Ghoul. "A Genetic Framework Model for Self-adaptive Software." Journal of Software Engineering 11, no. 3 (August 15, 2017): 255–65. http://dx.doi.org/10.3923/jse.2017.255.265.

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39

Tóth, Adél, and Péter Szeverényi. "Interpretation in reproductive genetic counseling: a methodological framework." Journal of Psychosomatic Obstetrics & Gynecology 28, no. 3 (January 2007): 141–45. http://dx.doi.org/10.1080/01674820701228185.

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40

Salvaggio, Philip S., John R. Schott, and Donald M. McKeown. "Genetic apertures: an improved sparse aperture design framework." Applied Optics 55, no. 12 (April 13, 2016): 3182. http://dx.doi.org/10.1364/ao.55.003182.

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41

Dao, Son Duy, Kazem Abhary, and Romeo Marian. "An innovative framework for designing genetic algorithm structures." Expert Systems with Applications 90 (December 2017): 196–208. http://dx.doi.org/10.1016/j.eswa.2017.08.018.

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42

Castelli, Mauro, Sara Silva, and Leonardo Vanneschi. "A C++ framework for geometric semantic genetic programming." Genetic Programming and Evolvable Machines 16, no. 1 (April 2, 2014): 73–81. http://dx.doi.org/10.1007/s10710-014-9218-0.

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43

Weil, Jon, and Ilana Mittman. "A teaching framework for cross-cultural genetic counseling." Journal of Genetic Counseling 2, no. 3 (September 1993): 159–69. http://dx.doi.org/10.1007/bf00962077.

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44

Santos, Aécio S. R., Cristiano R. de Carvalho, Jussara M. Almeida, Edleno S. de Moura, Altigran S. da Silva, and Nivio Ziviani. "A genetic programming framework to schedule webpage updates." Information Retrieval Journal 18, no. 1 (October 28, 2014): 73–94. http://dx.doi.org/10.1007/s10791-014-9248-5.

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45

Antunes, Alexandre P., and João Luiz F. Azevedo. "An aerodynamic optimization computational framework using genetic algorithms." Journal of the Brazilian Society of Mechanical Sciences and Engineering 38, no. 4 (November 2, 2015): 1037–58. http://dx.doi.org/10.1007/s40430-015-0445-y.

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46

Imade, Hiroaki, Ryohei Morishita, Isao Ono, Norihiko Ono, and Masahiro Okamoto. "A grid-oriented genetic algorithm framework for bioinformatics." New Generation Computing 22, no. 2 (June 2004): 177–86. http://dx.doi.org/10.1007/bf03040956.

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47

Lewis, Anna C. F., Santiago J. Molina, Paul S. Appelbaum, Bege Dauda, Agustin Fuentes, Stephanie M. Fullerton, Nanibaa’ A. Garrison, et al. "An Ethical Framework for Research Using Genetic Ancestry." Perspectives in Biology and Medicine 66, no. 2 (March 2023): 225–48. http://dx.doi.org/10.1353/pbm.2023.0021.

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48

Dorfman, Ruslan, Rabia Khan, and Gouri Mukerjee. "Proposed Regulatory Framework for Direct-to-Consumer Genetic Testing: Diagnostics vs Genetic Screening." Clinical Chemistry 60, no. 11 (November 1, 2014): 1455–56. http://dx.doi.org/10.1373/clinchem.2014.226993.

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49

Shumny, V. K. "Development of genetic research in the USSR." Genome 31, no. 2 (January 15, 1989): 900–904. http://dx.doi.org/10.1139/g89-160.

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Two periods of the development of genetic research in the USSR with reference to its current trends of plant and animal genetics, cytogenetics, and molecular genetics are reviewed. A short list of priority areas is established: the maintenance and use of unique gene pools of plants and animals; the domestication of animals and cultivation of new plants; the development of programmes for mathematical treatment of genetic data banks. It is suggested to consider them within the framework of international projects. The idea is to promote the collaborative efforts of scientists on an international scale.Key words: genetics in the USSR, current trends, international cooperation.

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50

Keller, Matthew C. "The Evolutionary Persistence of Genes That Increase Mental Disorders Risk." Current Directions in Psychological Science 17, no. 6 (December 2008): 395–99. http://dx.doi.org/10.1111/j.1467-8721.2008.00613.x.

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Natural selection constantly removes those genetic variants (alleles) that even slightly decrease average reproductive success. Yet, given the high heritabilities and prevalence rates of severe mental disorders, human populations seem to be awash in deleterious alleles. Evolutionary genetics offers an illuminating framework for understanding why mental disorder risk alleles have persisted despite natural selection, and this framework can help guide future research in behavioral and psychiatric genetics.

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