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Zeitschriftenartikel zum Thema „Genetic“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 26. Oktober 2024

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1

Sumida, Brian. „Genetics for genetic algorithms“. ACM SIGBIO Newsletter 12, Nr. 2 (Juni 1992): 44–46. http://dx.doi.org/10.1145/130686.130694.

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2

Niendorf, Kristin Baker. „Genetic Library: Cancer Genetics“. Journal of Genetic Counseling 11, Nr. 5 (Oktober 2002): 429–34. http://dx.doi.org/10.1023/a:1016854001384.

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3

Comfort, Nathaniel. „Genetics: The genetic watchmaker“. Nature 502, Nr. 7472 (Oktober 2013): 436–37. http://dx.doi.org/10.1038/502436a.

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4

Clarke, Angus. „Genetic imprinting in clinical genetics“. Development 108, Supplement (01.04.1990): 131–39. http://dx.doi.org/10.1242/dev.108.supplement.131.

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Genetic, and indeed genomic, imprinting does occur in humans. This is manifest at the level of the genome, the individual chromosome, subchromosomal region or fragile site, or the single locus. The best evidence at the single gene level comes from a consideration of familial tumour syndromes. Chromosomal imprinting effects are revealed when uniparental disomy occurs, as in the Prader-Willi syndrome and doubtless other sporadic, congenital anomaly syndromes. Genomic imprinting is manifest in the developmental defects of hydatidiform mole, teratoma and triploidy. Fragile (X) mental retardation shows an unusual pattern of inheritance, and imprinting can account for these effects. Future work in clinical genetics may identify congenital anomalies and growth disorders caused by imprinting: the identification of imprinting effects for specific chromosomal regions in mice will allow the examination of the homologous chromosomal region in humans.
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Kallab, Chadi, Samir Haddad und Jinane Sayah. „Flexible Traceable Generic Genetic Algorithm“. Open Journal of Applied Sciences 12, Nr. 06 (2022): 877–91. http://dx.doi.org/10.4236/ojapps.2022.126060.

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6

Shanmugam, Ramalingam. „Biostatistical genetics and genetic epidemiology“. Journal of Statistical Computation and Simulation 73, Nr. 7 (Juli 2003): 543–44. http://dx.doi.org/10.1080/0094965021000044411.

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7

Siegel, PB, und EA Dunnington. „Genetic selection strategies–population genetics“. Poultry Science 76, Nr. 8 (August 1997): 1062–65. http://dx.doi.org/10.1093/ps/76.8.1062.

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8

Athanasiou, Y., M. Zavros, M. Arsali, L. Papazachariou, P. Demosthenous, I. Savva, K. Voskarides et al. „GENETIC DISEASES AND MOLECULAR GENETICS“. Nephrology Dialysis Transplantation 29, suppl 3 (01.05.2014): iii339—iii350. http://dx.doi.org/10.1093/ndt/gfu162.

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9

Ziegel, Eric R. „Biostatistical Genetics and Genetic Epidemiology“. Technometrics 44, Nr. 4 (November 2002): 409. http://dx.doi.org/10.1198/tech.2002.s98.

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10

Neville, Melvin, und Anaika Sibley. „Developing a generic genetic algorithm“. ACM SIGAda Ada Letters XXIII, Nr. 1 (März 2003): 45–52. http://dx.doi.org/10.1145/1066404.589462.

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11

Elias, Sherman, und George J. Annas. „Generic Consent for Genetic Screening“. New England Journal of Medicine 330, Nr. 22 (02.06.1994): 1611–13. http://dx.doi.org/10.1056/nejm199406023302213.

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12

Stekrova, J., J. Reiterova, V. Elisakova, M. Merta, M. Kohoutova, V. Tesar, S. Suvakov et al. „Genetic diseases and molecular genetics“. Clinical Kidney Journal 4, suppl 2 (01.06.2011): 4.s2.28. http://dx.doi.org/10.1093/ndtplus/4.s2.28.

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13

Caron, Paul R. „Genetic approach to chemical genetics ▾“. Drug Discovery Today 7, Nr. 22 (November 2002): 1121. http://dx.doi.org/10.1016/s1359-6446(02)02506-0.

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14

Legendre, C., D. Cohen, Y. Delmas, T. Feldkamp, D. Fouque, R. Furman, O. Gaber et al. „Genetic diseases and molecular genetics“. Nephrology Dialysis Transplantation 28, suppl 1 (01.05.2013): i309—i321. http://dx.doi.org/10.1093/ndt/gft126.

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15

Smith, Douglas M. „Genetic testingAbout epilepsy and genetics“. Neurology 92, Nr. 5 (28.01.2019): e523-e526. http://dx.doi.org/10.1212/wnl.0000000000006863.

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16

Shah, Ebrahim. „Biostatistical Genetics and Genetic Epidemiology.“ International Journal of Epidemiology 32, Nr. 3 (Juni 2003): 474. http://dx.doi.org/10.1093/ije/dyg171.

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17

Raol, Jitendra R., und Abhijit Jalisatgi. „From genetics to genetic algorithms“. Resonance 1, Nr. 8 (August 1996): 43–54. http://dx.doi.org/10.1007/bf02837022.

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18

Morin-Chassé, Alexandre. „Behavioral Genetics, Population Genetics, and Genetic Essentialism“. Science & Education 29, Nr. 6 (04.11.2020): 1595–619. http://dx.doi.org/10.1007/s11191-020-00166-y.

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19

Famuji, Tri Stiyo, Herman Herman und Sunardi Sunardi. „Smart Contract Penyimpanan Data Genetika Manusia Berbiaya Murah pada Blockchain Ethereum“. Jurnal Teknologi Informasi dan Ilmu Komputer 11, Nr. 3 (31.07.2024): 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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20

Lynch, Henry T., und Jane F. Lynch. „Breast cancer genetics: Family history, heterogeneity, molecular genetic diagnosis, and genetic counseling“. Current Problems in Cancer 20, Nr. 6 (November 1996): 329–65. http://dx.doi.org/10.1016/s0147-0272(96)80010-9.

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21

Brower, Vicki. „From Genetic Systems to Seattle Genetics“. Nature Biotechnology 16, Nr. 6 (Juni 1998): 508. http://dx.doi.org/10.1038/nbt0698-508b.

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22

Burke, Donald S., Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey und Annie S. Wu. „Putting More Genetics into Genetic Algorithms“. Evolutionary Computation 6, Nr. 4 (Dezember 1998): 387–410. http://dx.doi.org/10.1162/evco.1998.6.4.387.

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The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.
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23

Megson, G. M., und I. M. Bland. „Generic systolic array for genetic algorithms“. IEE Proceedings - Computers and Digital Techniques 144, Nr. 2 (1997): 107. http://dx.doi.org/10.1049/ip-cdt:19971126.

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24

Lerner, Barbara, Carol Christianson, Lori Engler-Todd, Sara Goldman, Karen Greendale, Julianne M. O'Daniel, Myra I. Roche und Kerry Silvey. „Genetic Library: Genetics and Public Health“. Journal of Genetic Counseling 13, Nr. 3 (18.05.2004): 259–66. http://dx.doi.org/10.1023/b:jogc.0000027960.06945.48.

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25

Miller, Matthew A., John H. Fingert und Daniel I. Bettis. „Genetics and genetic testing for glaucoma“. Current Opinion in Ophthalmology 28, Nr. 2 (März 2017): 133–38. http://dx.doi.org/10.1097/icu.0000000000000344.

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26

Hamby, Lori, und Constance A. Griffin. „Genetic Library Video Reviews: Cancer Genetics“. Journal of Genetic Counseling 12, Nr. 2 (April 2003): 185–92. http://dx.doi.org/10.1023/a:1022615408076.

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27

Harris, Rodney. „Genetic counselling and the new genetics“. Trends in Genetics 4, Nr. 2 (Februar 1988): 52–56. http://dx.doi.org/10.1016/0168-9525(88)90067-4.

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28

Philip, Adejumo A. „P3-456: Genetics and genetic testing“. Alzheimer's & Dementia 4 (Juli 2008): T655. http://dx.doi.org/10.1016/j.jalz.2008.05.2027.

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29

Lois, Carlos, und James O. Groves. „Genetics in non-genetic model systems“. Current Opinion in Neurobiology 22, Nr. 1 (Februar 2012): 79–85. http://dx.doi.org/10.1016/j.conb.2011.11.002.

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30

Lunn, Mitchell R., und Brent R. Stockwell. „Chemical Genetics and Orphan Genetic Diseases“. Chemistry & Biology 12, Nr. 10 (Oktober 2005): 1063–73. http://dx.doi.org/10.1016/j.chembiol.2005.09.005.

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31

Sprenger, G. A., M. A. Typas und C. Drainas. „Genetics and genetic engineering ofZymomonas mobilis“. World Journal of Microbiology & Biotechnology 9, Nr. 1 (Januar 1993): 17–24. http://dx.doi.org/10.1007/bf00656509.

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32

Yan, H. „GENETICS: Genetic Testing- Present and Future“. Science 289, Nr. 5486 (15.09.2000): 1890–92. http://dx.doi.org/10.1126/science.289.5486.1890.

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33

Wierzbicki, Anthony S. „Genetics and molecular biology: Genetic epidemiology“. Current Opinion in Lipidology 15, Nr. 6 (Dezember 2004): 699–701. http://dx.doi.org/10.1097/00041433-200412000-00011.

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34

Jamieson, Annie, und Gregory Radick. „Genetic Determinism in the Genetics Curriculum“. Science & Education 26, Nr. 10 (06.07.2017): 1261–90. http://dx.doi.org/10.1007/s11191-017-9900-8.

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35

Nakamura, Yusuke. „Approaching genetic diseases by “reverse genetics”“. Japanese journal of human genetics 35, Nr. 1 (März 1990): 20–21. http://dx.doi.org/10.1007/bf01883169.

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36

Bishop, Kathleen Kirk. „Psychosocial Aspects of Genetic Disorders: Implications for Practice“. Families in Society: The Journal of Contemporary Social Services 74, Nr. 4 (April 1993): 207–12. http://dx.doi.org/10.1177/104438949307400402.

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Generic disorders can potentially interfere with interpersonal relationships and normal social develop' ment as well as disrupt family life. As scientific and technological advances in medical genetics provide health professionals with a more comprehensive understanding of the origin, implications, and management of genetic disorders, professionals acquire expanded responsibilities. Social workers, who are often involved with individuals and families on a long-term basis, play an instrumental role in helping individuals and families make the necessary emotional and social adjustments following diagnosis of a genetic disease, understand the ramifications of the diagnosis, cope with the accompanying concerns, and find me appropriate services.
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37

Jensen, Pamela. „Genetic Privacy: The Potential for Genetic Discrimination in Insurance“. Victoria University of Wellington Law Review 29, Nr. 2 (01.04.1999): 347. http://dx.doi.org/10.26686/vuwlr.v29i2.6035.

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The threat of modern genetics has been perceived as coming, rather dramatically, from genetic engineering, but the less flashy field of medical genetic testing poses significant and immediate issues. This article discusses the potential for breach of confidentiality or invasion of privacy through the acquisition of information, the disclosure of information, and the potential for prejudicial use of that information by third parties. The author concludes that New Zealand's ethical and legal aspects of human genetics needed a review at the time of writing, recommending an advisory group to be set up to monitor developments in human genetics, facilitate discussion with all relevant persons, groups and bodies, and report on issues arising from new developments in human genetics that can be expected to have wider ethical, social, economic, and legal consequences. However, the author does not find it necessary to enact genetic-specific legislation.
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38

Maryuningsih, Yuyun, Topik Hidayat, R. Riandi und Nuryani Y. Rustaman. „Application of genetic problem base online discussion to improve genetic literacy of prospective teachers“. JPBI (Jurnal Pendidikan Biologi Indonesia) 8, Nr. 1 (31.03.2022): 65–76. http://dx.doi.org/10.22219/jpbi.v8i1.19035.

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Genetics is a subject that is quite difficult according to students. Various strategies and methods are used to understand genetics in learning to have genetic literacy. One way of increasing genetic literacy in students is to apply genetic problems based on an online discussion in genetics lectures. The research was conducted to determine the effect of genetic problem-based online discussion on increasing students' genetic literacy. The research design used a pre-posttest control group design. It was carried out experimentally on three treatment groups: the genetic problem base of students, the genetic problem base of educators - students, and the genetic problem base of educators. According to the genetic literacy domain, genetic literacy is measured through multiple-choice tests, including genetic models, meiotic models, and molecular models. Manova analyzed the value of gene literacy, and a post-doc further test was performed to differentiate genetic literacy in the three treatment groups. The results showed that genetic literacy increased in all treatment groups, with the highest increase in the group that applied a genetic problem base focused on student problems.
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39

Kučera, L. „D.C. Rao & M.A. Province – Advances in Genetics,Vol. 42, Genetic Dissection of Complex Traits“. Czech Journal of Genetics and Plant Breeding 38, No. 1 (30.07.2012): 64. http://dx.doi.org/10.17221/6112-cjgpb.

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40

Bokkers, Kyra, Michiel Vlaming, Ellen G. Engelhardt, Ronald P. Zweemer, Inge M. van Oort, Lambertus A. L. M. Kiemeney, Eveline M. A. Bleiker und Margreet G. E. M. Ausems. „The Feasibility of Implementing Mainstream Germline Genetic Testing in Routine Cancer Care—A Systematic Review“. Cancers 14, Nr. 4 (19.02.2022): 1059. http://dx.doi.org/10.3390/cancers14041059.

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Background: Non-genetic healthcare professionals can provide pre-test counseling and order germline genetic tests themselves, which is called mainstream genetic testing. In this systematic review, we determined whether mainstream genetic testing was feasible in daily practice while maintaining quality of genetic care. Methods: PubMed, Embase, CINAHL, and PsychINFO were searched for articles describing mainstream genetic testing initiatives in cancer care. Results: Seventeen articles, reporting on 15 studies, met the inclusion criteria. Non-genetic healthcare professionals concluded that mainstream genetic testing was possible within the timeframe of a routine consultation. In 14 studies, non-genetic healthcare professionals completed some form of training about genetics. When referral was coordinated by a genetics team, the majority of patients carrying a pathogenic variant were seen for post-test counseling by genetic healthcare professionals. The number of days between cancer diagnosis and test result disclosure was always lower in the mainstream genetic testing pathway than in the standard genetic testing pathway (e.g., pre-test counseling at genetics department). Conclusions: Mainstream genetic testing seems feasible in daily practice with no insurmountable barriers. A structured pathway with a training procedure is desirable, as well as a close collaboration between genetics and other clinical departments.
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Siváková, Daniela, Marta Cvíčelová, Karol Hatiar und Hubert Walter. „Genetic studies in a North Slovakian isolate: Chmel'nica. 4. Anthropometric, genetical and behavioural characters“. Zeitschrift für Morphologie und Anthropologie 80, Nr. 3 (16.11.1995): 361–70. http://dx.doi.org/10.1127/zma/80/1995/361.

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42

Paaby, Annalise, und Greg Gibson. „Cryptic Genetic Variation in Evolutionary Developmental Genetics“. Biology 5, Nr. 2 (13.06.2016): 28. http://dx.doi.org/10.3390/biology5020028.

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43

Curnow, R. N., A. H. D. Brown, M. T. Clegg, A. L. Kahler und B. S. Weir. „Plant Population Genetics, Breeding, and Genetic Resources.“ Biometrics 46, Nr. 4 (Dezember 1990): 1241. http://dx.doi.org/10.2307/2532478.

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44

Wang, Guoliang, Ruirui Ji, Wenxin Zou, Daniel J. Penny und Yuxin Fan. „Inherited Cardiomyopathies: Genetics and Clinical Genetic Testing“. Cardiovascular Innovations and Applications 2, Nr. 2 (01.02.2017): 297–308. http://dx.doi.org/10.15212/cvia.2017.0015.

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45

Kuchuk, N. V. „Cell genetic engineering: Transmission genetics of plants“. Cytology and Genetics 51, Nr. 2 (März 2017): 103–7. http://dx.doi.org/10.3103/s0095452717020062.

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46

Aslamkhan, Muhammad. „Clinical Genetics and Genetic Counselling in Pakistan“. Journal of Genes and Cells 1, Nr. 2 (02.04.2015): 31. http://dx.doi.org/10.15562/gnc.17.

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47

FRANK, D. W., und T. C. ZAHRT. „Genetics and Genetic Manipulation in Francisella Tularensis“. Annals of the New York Academy of Sciences 1105, Nr. 1 (29.03.2007): 67–97. http://dx.doi.org/10.1196/annals.1409.008.

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48

Dawes, Ian W. „Yeast genetics: Genetic control mechanisms: transcriptional twisting“. Nature 324, Nr. 6094 (November 1986): 214. http://dx.doi.org/10.1038/324214a0.

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49

Moreno, Victor. „Book Review: Biostatistical genetics and genetic epidemiology“. Statistical Methods in Medical Research 14, Nr. 1 (Februar 2005): 115–16. http://dx.doi.org/10.1177/096228020501400109.

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50

Lacombe, Didier. „Teeth anomalies and genetics, including genetic syndromes“. European Journal of Human Genetics 22, Nr. 11 (16.10.2014): 1339. http://dx.doi.org/10.1038/ejhg.2014.98.

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