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Thematische Bibliographien / Gene selection

Auswahl der wissenschaftlichen Literatur zum Thema „Gene selection“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Inhaltsverzeichnis

Zeitschriftenartikel
Dissertationen
Bücher
Buchteile
Konferenzberichte
Berichte der Organisationen

Zeitschriftenartikel zum Thema "Gene selection":

1

Liu, Junjie, Peng Li, Liuyang Lu, Lanfen Xie, Xiling Chen und Baizhong Zhang. „Selection and evaluation of potential reference genes for gene expression analysis in Avena fatua Linn“. Plant Protection Science 55, No. 1 (20.11.2018): 61–71. http://dx.doi.org/10.17221/20/2018-pps.

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Eight commonly used candidate reference genes, 18S ribosomal RNA (rRNA) (18S), 28S rRNA (28S), actin (ACT), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), elongation factor 1 alpha (EF1α), ribosomal protein L7 (RPL7), Alpha-tubulin (α-TUB), and TATA box binding protein-associated factor (TBP), were evaluated under various experimental conditions to assess their suitability in different developmental stages, tissues and herbicide treatments in Avena fatua. The results indicated the most suitable reference genes for the different experimental conditions. For developmental stages, 28S and EF1α were the optimal reference genes, both EF1α and 28S were suitable for experiments of different tissues, whereas for herbicide treatments, GAPDH and ACT were suitable for normalizations of expression data. In addition, GAPDH and EF1α were the suitable reference genes.

2

R, Dr Prema. „Feature Selection for Gene Expression Data Analysis – A Review“. International Journal of Psychosocial Rehabilitation 24, Nr. 5 (25.05.2020): 6955–64. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020695.

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3

Lee, K. E., N. Sha, E. R. Dougherty, M. Vannucci und B. K. Mallick. „Gene selection: a Bayesian variable selection approach“. Bioinformatics 19, Nr. 1 (01.01.2003): 90–97. http://dx.doi.org/10.1093/bioinformatics/19.1.90.

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4

Klee, Eric W., Stephen C. Ekker und Lynda B. M. Ellis. „Target selection forDanio rerio functional genomics“. genesis 30, Nr. 3 (2001): 123–25. http://dx.doi.org/10.1002/gene.1045.

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5

Tsakas, SC. „Species versus gene selection“. Genetics Selection Evolution 21, Nr. 3 (1989): 247. http://dx.doi.org/10.1186/1297-9686-21-3-247.

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6

Greenspan, R. J. „Selection, Gene Interaction, and Flexible Gene Networks“. Cold Spring Harbor Symposia on Quantitative Biology 74 (01.01.2009): 131–38. http://dx.doi.org/10.1101/sqb.2009.74.029.

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7

D., Saravanakumar. „Improving Microarray Data Classification Using Optimized Clustering-Based Hybrid Gene Selection Algorithm“. Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (28.02.2020): 486–95. http://dx.doi.org/10.5373/jardcs/v12sp3/20201283.

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8

Nesvadbová, M., und A. Knoll. „Evaluation of reference genes for gene expression studies in pig muscle tissue by real-time PCR“. Czech Journal of Animal Science 56, No. 5 (30.05.2011): 213–16. http://dx.doi.org/10.17221/1428-cjas.

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The selection of reference genes is essential for gene expression studies when using a real-time quantitative polymerase chain reaction (PCR). Reference gene selection should be performed for each experiment because the gene expression level may be changed in different experimental conditions. In this study, the stability of mRNA expression was determined for seven genes: HPRT1, RPS18, NACA, TBP, TAF4B, RPL32 and OAZ1. The stability of these reference genes was investigated in the skeletal muscle tissue of pig foetuses, piglets and adult pigs using real-time quantitative PCR and SYBR green chemistry. The expression of stability of the used reference genes was calculated using the geNorm application. Different gene expression profiles among the age categories of pigs were found out. RPS18 has been identified as the gene with the most stable expression in the muscle tissue of all pig age categories. HPRT1 and RPL32 were found to have the highest stability in piglets and adult pigs, and in foetuses and adults pigs, respectively. The newly used reference gene, TAF4B, reached the highest expression stability in piglets.

9

Gilad, Yoav, Alicia Oshlack und Scott A. Rifkin. „Natural selection on gene expression“. Trends in Genetics 22, Nr. 8 (August 2006): 456–61. http://dx.doi.org/10.1016/j.tig.2006.06.002.

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10

Behar, Hilla, und Marcus W. Feldman. „Gene-culture coevolution under selection“. Theoretical Population Biology 121 (Mai 2018): 33–44. http://dx.doi.org/10.1016/j.tpb.2018.03.001.

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Dissertationen zum Thema "Gene selection":

1

Petronella, Nicholas. „Gene Conversions and Selection in the Gene Families of Primates“. Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20538.

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We used the GENECONV program, the Hsu et al. (2010) method and phylogenetic analyses to analyze the gene conversions which occurred in the growth hormone, folate receptor and trypsin gene families of six primate species. Significant positive correlations were found between sequence similarity and conversion length in all but the trypsin gene family. Converted regions, when compared to non-converted ones, also displayed a significantly higher GC-content in the growth hormone and folate receptor gene families. Finally, all detected gene conversions were found to be less frequent in conserved gene regions and towards functionally important genes. This suggests that purifying selection is eliminating all gene conversions having a negative functional impact.

2

Zid, Mouldi. „Gene Conversions in the Siglec and CEA Immunoglobulin Gene Families of Primates“. Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23625.

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Siglecs and CEA are two families of cell surface proteins belonging to the immunoglobulin superfamily. They are thought to be involved in cell-cell interactions and have various other biological functions. We used the GENECONV program that applies statistical tests to detect gene conversion events in each family of five primate species. For the Siglec family, we found that gene conversions are frequent between CD33rSiglec genes, but are absent between their conserved Siglec genes. For the CEA family, half of gene conversion events detected are located in coding regions. A significant positive correlation was found between the length of the conversions and the similarity of the converted regions only in the Siglec gene family. Moreover, we found an increase in GC-content and similarity in converted regions compared to non-converted regions of the two families. Furthermore, in the two families, gene conversions occur more frequently in the extracellular domains of proteins, and rarely in their transmembrane and cytoplasmic regions. Finally, these two families appear to be evolving neutrally or under negative selection.

3

Liu, Zhilin. „Gene expression profiling of bovine ovarian follicular selection“. Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4490.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pf file; the official abstract appears in the short.pf file (which also appears in the research.pf); a non-technical general description, or public abstract, appears in the public.pf file. Title from title screen of research.pf file (viewed on May 6, 2009) Vita. Includes bibliographical references.

4

Huisman, Jisca. „Gene Flow and Natural Selection in Atlantic Salmon“. Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for biologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-16991.

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5

Chen, Li. „Ranking-Based Methods for Gene Selection in Microarray Data“. Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3888.

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DNA microarrays have been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. One of the major goals of microarray data analysis is the detection of differentially expressed genes across two kinds of tissue samples or samples obtained under two experimental conditions. A large number of gene detection methods have been developed and most of them are based on statistical analysis. However the statistical analysis methods have the limitations due to the small sample size and unknown distribution and error structure of microarray data. In this thesis, a study of ranking-based gene selection methods which have weak assumption about the data was done. Three approaches are proposed to integrate the individual ranks to select differentially expressed genes in microarray data. The experiments are implemented on the simulated and biological microarray data, and the results show that ranking-based methods outperform the t-test and SAM in selecting differentially expressed genes, especially when the sample size is small.

6

Medeiros, Lucas Paoliello de. „Coevolution in mutualistic networks: gene flow and selection mosaics“. Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/41/41134/tde-17102017-154829/.

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Ecological interactions such as predation, competition, and mutualism are important forces that influence species evolution. Coevolution is defined as reciprocal evolutionary change in interacting species. The Geographic Mosaic Theory of Coevolution (GMTC) provides a theoretical framework to explain how collections of populations should coevolve across space. Two fundamental aspects of the GMTC are gene flow among populations and the presence of selection mosaics, which are collections of localities with particular selection regimes. Several studies have explored how phenotypic trait matching between species evolves in pairs or small groups of species. However, ecological interactions frequently form large networks that connect dozens of species present in a given community. In networks of mutualisms, for instance, the organization of interactions may affect ecological and evolutionary processes. A next step in understanding the coevolutionary process is to investigate how aspects of the GMTC affect the evolution of species embedded in interaction networks. In this dissertation, we tried to fill this gap using a mathematical model of coevolution, complex networks tools, and information on empirical mutualistic networks. Our numerical simulations of the coevolutionary model allow us to draw three main conclusions. First, gene flow affects trait patterns generated by coevolution and may favor the emergence of trait matching depending on the selection mosaic. Second, the organization of mutualistic networks influences coevolution, but this effect may vanish when gene flow favors trait matching. Intimate mutualisms, such as protection of host plants by ants, form small and compartmentalized networks that generate higher trait matching than large and nested networks typical of mutualisms among free-living species, such as pollination. Third, habitat fragmentation resulting in the disruption of gene flow should reduce the reciprocal adaptations between interacting species and at the same time promote adaptations to the local abiotic environment. In conclusion, we show that a complex interplay between gene flow, the geographic structure of selection, and the organization of ecological networks shapes the evolution of large groups of species. Our results therefore allow predictions of how environmental impacts such as habitat fragmentation will modify the evolution of species interactions
Interações ecológicas como predação, competição e mutualismo são importantes forças que influenciam a evolução de espécies. Chamamos de coevolução a mudança evolutiva recíproca em espécies que interagem. A Teoria do Mosaico Geográfico da Coevolução (TMGC) fornece um arcabouço teórico para entender como conjuntos de populações coevoluem ao longo do espaço. Dois aspectos fundamentais da TMGC são o fluxo gênico entre populações e a presença de mosaicos de seleção, isto é, conjuntos de locais com regimes de seleção particulares. Diversos estudos exploraram como o acoplamento entre fenótipos de diferentes espécies evolui em pares ou pequenos grupos de espécies. Entretanto, interações ecológicas frequentemente formam grandes redes que conectam dezenas de espécies presentes em uma comunidade. Em redes de mutualismos, por exemplo, a organização das interações pode influenciar processos ecológicos e evolutivos. Um próximo passo para a compreensão do processo coevolutivo consiste em investigar como aspectos da TMGC influenciam a evolução de espécies em redes de interações. Nesta dissertação, tentamos preencher esta lacuna usando um modelo matemático de coevolução, ferramentas de redes complexas e informação sobre redes mutualistas empíricas. Nossas simulações numéricas do modelo coevolutivo apontam para três principais conclusões. Primeiro, o fluxo gênico influencia os padrões fenotípicos gerados por coevolução e pode favorecer a emergência de acoplamento fenotípico entre espécies dependendo do mosaico de seleção. Segundo, a organização de redes mutualistas influencia a coevolução, mas este efeito pode desaparecer quando o fluxo gênico favorece acoplamento fenotípico. Mutualismos íntimos, como proteção de plantas hospedeiras por formigas, formam redes pequenas e compartimentalizadas que geram um maior acoplamento fenotípico do que as redes grandes e aninhadas típicas de mutualismos entre espécies de vida livre, como polinização. Por fim, a fragmentação de habitat, ao extinguir o fluxo gênico, pode reduzir as adaptações recíprocas entre espécies e ao mesmo tempo tornar cada espécie mais adaptada ao seu ambiente abiótico local. Em suma, mostramos que interações complexas entre fluxo gênico, estrutura geográfica da seleção e organização de redes ecológicas moldam a evolução de grandes grupos de espécies. Dessa forma, podemos traçar previsões sobre como impactos ambientais como a fragmentação de habitat irão alterar a evolução de interações ecológicas

7

Dai, Xiaotian. „Novel Statistical Models for Quantitative Shape-Gene Association Selection“. DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6856.

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Other research reported that genetic mechanism plays a major role in the development process of biological shapes. The primary goal of this dissertation is to develop novel statistical models to investigate the quantitative relationships between biological shapes and genetic variants. However, these problems can be extremely challenging to traditional statistical models for a number of reasons: 1) the biological phenotypes cannot be effectively represented by single-valued traits, while traditional regression only handles one dependent variable; 2) in real-life genetic data, the number of candidate genes to be investigated is extremely large, and the signal-to-noise ratio of candidate genes is expected to be very high. In order to address these challenges, we propose three statistical models to handle multivariate, functional, and multilevel functional phenotypes, with applications to biological shape data using different shape descriptors. To the best of our knowledge, there is no statistical model developed for multilevel functional phenotypes. Even though multivariate regressions have been well-explored and these approaches can be applied to genetic studies, we show that the model proposed in this dissertation can outperform other alternatives regarding variable selection and prediction through simulation examples and real data examples. Although motivated ultimately by genetic research, the proposed models can be used as general-purpose machine learning algorithms with far-reaching applications.

8

Perucchini, Matteo. „The cervid PrP gene : patterns of variability and selection“. Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/15634.

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Variation at codon 132 of the Cervus canadensis (wapiti) PRNP has been claimed to modulate Chronic Wasting Disease (CWD), a relatively new TSE affecting cervid species and currently the only TSE naturally affecting both captive and free-ranging populations. Codon 132 corresponds to the human codon 129 and variation at this position has been associated with TSE-related balancing selection in humans. This thesis investigated the genetic variability and selective patterns of coding and non-coding regions of PRNP in free-ranging populations of C. Canadensis and C. elaphus (CWD-free species closely related to wapiti) to gain a better understanding of the possible functional or disease-related forces shaping PrP genetics. The study of codon 132 genotype patterns in CWD_+VEand CWD_-VE wapiti provided no evidence for genetic modulation of CWD susceptibility, challenging previously published data. Despite this, a modulatory role of this residue in CWD incubation time, as suggested by many, is still possible. The analysis of the variability patterns in the PrP gene of the two cervid species suggested the presence of purifying selection. This was also supported by analyses aimed at identifying positively selected sites, which showed that codon 100 was the only site under positive selection throughout mammalian evolution, while the rest of the protein was under strong purifying selection. These data provide further support for the hypothesis suggesting a key cellular role for the PrP protein. The adaptive pressures driving selection at codon 100 are unknown, although they are most likely to be related to PrP function. A role for variation at this position in the interaction of the PrP protein with cell membrane translocation factors is proposed. The study provided an insight into the possible forces shaping PrP genetics and revaluated the role of variation at codon 132 in the wapiti PRNP gene in relation to CWD susceptibility.

9

Riddoch, B. „Selection component analysis of the PGI polymorphism in Sphaeroma rugicauda“. Thesis, University of Essex, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378440.

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10

Panji, Sumir. „Identification of bacterial pathogenic gene classes subject to diversifying selection“. Thesis, University of the Western Cape, 2009. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_5842_1297942831.

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Availability of genome sequences for numerous bacterial species comprising of different bacterial strains allows elucidation of species and strain specific adaptations that facilitate their survival in widely fluctuating micro-environments and enhance their pathogenic potential. Different bacterial species use different strategies in their pathogenesis and the pathogenic potential of a bacterial species is dependent on its genomic complement of virulence factors. A bacterial virulence factor, within the context of this study, is defined as any endogenous protein product encoded by a gene that aids in the adhesion, invasion, colonization, persistence and pathogenesis of a bacterium within a host. Anecdotal evidence suggests that bacterial virulence genes are undergoing diversifying evolution to counteract the rapid adaptability of its host&rsquo
s immune defences. Genome sequences of pathogenic bacterial species and strains provide unique opportunities to study the action of diversifying selection operating on different classes of bacterial genes.

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Bücher zum Thema "Gene selection":

1

Collins, Warwick. A silent gene theory of evolution. Buckingham, UK: University of Buckingham Press, 2009.

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2

Roughgarden, Joan. The genial gene: Deconstructing Darwinian selfishness. Berkeley: University of California Press, 2009.

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3

Silson, Roy G. Additive gene systems: An explanation for problems in evolution and selection. Herts: Greenfield, 1988.

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4

Wingerson, Lois. Unnatural selection: The promise and the power of human gene research. New York: Bantam Books, 1998.

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5

Wingerson, Lois. Unnatural selection: The promise and the power of human gene research. New York: Bantam Books, 1999.

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6

Foster, Charles A. The selfless gene: Living with God and Darwin. Nashville, Tenn: Thomas Nelson, 2009.

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7

Foster, Charles A. The selfless gene: Living with God and Darwin. Nashville, Tenn: Thomas Nelson, 2009.

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8

Dawkins, Richard. The extended phenotype: The long reach of the gene. Oxford: Oxford University Press, 1989.

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9

Dawkins, Richard. The Extended Phenotype: The long reach of the gene. Oxford: Oxford University Press, 1999.

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10

Dawkins, Richard. The Selfish Gene. Oxford: Oxford University Press, 1999.

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Buchteile zum Thema "Gene selection":

1

Kriegler, Michael. „Selection and Amplification“. In Gene Transfer and Expression, 103–13. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-11891-5_6.

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2

Goodnight, Charles J. „Gene Interaction and Selection“. In Plant Breeding Reviews, 269–91. Oxford, UK: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470650240.ch12.

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3

Rodriguez-Grande, Jorge, und Raul Fernandez-Lopez. „Measuring Plasmid Conjugation Using Antibiotic Selection“. In Horizontal Gene Transfer, 93–98. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-4939-9877-7_6.

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4

Bradshaw, John E. „Gene Expression and Selection of Major Genes“. In Plant Breeding: Past, Present and Future, 133–59. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23285-0_5.

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5

Rothenberg, S. Michael, Joan Fisher, David Zapol, David Anderson, Yasumichi Hitoshi, Philip Achacoso und Gany P. Nolan. „Intracellular Combinatorial Chemistry with Peptides in Selection of Caspase-like Inhibitors“. In Gene Therapy, 171–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72160-1_18.

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6

Hust, Michael, André Frenzel, Thomas Schirrmann und Stefan Dübel. „Selection of Recombinant Antibodies from Antibody Gene Libraries“. In Gene Function Analysis, 305–20. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-721-1_14.

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Hust, Michael, Stefan Dübel und Thomas Schirrmann. „Selection of Recombinant Antibodies From Antibody Gene Libraries“. In Gene Function Analysis, 243–55. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-547-3_14.

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8

Borges, Helyane Bronoski, und Julio Cesar Nievola. „Gene Selection from Microarray Data“. In Intelligent Text Categorization and Clustering, 1–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85644-3_1.

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Mirzal, Andri. „SVD Based Gene Selection Algorithm“. In Lecture Notes in Electrical Engineering, 223–30. Singapore: Springer Singapore, 2013. http://dx.doi.org/10.1007/978-981-4585-18-7_26.

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10

Hudson, Richard R., und Norman L. Kaplan. „Gene Trees with Background Selection“. In Non-Neutral Evolution, 140–53. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2383-3_12.

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Konferenzberichte zum Thema "Gene selection":

1

Aouf, Mohamad, Amr Sharawi, Khaled Samir, Sultan Almotatiri, Abdulla Bajahzar und Ghada Kareem. „Gene Expression Data For Gene Selection Using Ensemble Based Feature Selection“. In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2019. http://dx.doi.org/10.1109/icicis46948.2019.9014722.

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2

Marvi-Khorasani, Hanieh, und Hamid Usefi. „Feature Clustering Towards Gene Selection“. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00240.

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Yildiz, Oktay, Mesut Tez, H. Sakir Bilge, M. Ali Akcayol und Inan Guler. „Gene selection for breast cancer“. In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204693.

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4

Wang, Fei, und Tao Li. „Gene Selection via Matrix Factorization“. In 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375686.

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5

Mitra, P., und D. D. Majumder. „Feature selection and gene clustering from gene expression data“. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334213.

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6

Liu, Quanzhong, Yang Zhang, Yong Wang und Zhengguo Hu. „Study of Informative Gene Selection for Gene Expression Profiles“. In 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.94.

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Wang, Shulin, Huowang Chen und Shutao Li. „Gene Selection Using Neighborhood Rough Set from Gene Expression Profiles“. In 2007 International Conference on Computational Intelligence and Security (CIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/cis.2007.169.

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8

Qi, Jianlong, und Jian Tang. „Gene Ontology Driven Feature Selection from Microarray Gene Expression Data“. In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330968.

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9

Lancucki, Adrian, Indrajit Saha und Piotr Lipinski. „A new evolutionary gene selection technique“. In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7257080.

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Banu, P. K. Nizar, und S. Andrews. „Informative Gene Selection - An evolutionary approach“. In 2013 International Conference on Current Trends in Information Technology (CTIT). IEEE, 2013. http://dx.doi.org/10.1109/ctit.2013.6749491.

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Berichte der Organisationen zum Thema "Gene selection":

1

Hayward, Simon W. Therapy Selection by Gene Profiling. Fort Belvoir, VA: Defense Technical Information Center, Mai 2008. http://dx.doi.org/10.21236/ada491350.

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2

Hayward, Simon W. Therapy Selection by Gene Profiling. Fort Belvoir, VA: Defense Technical Information Center, April 2004. http://dx.doi.org/10.21236/ada426169.

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3

Hayward, Simon W. Therapy Selection by Gene Profiling. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada454306.

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4

Curiel, David T., Gene Siegal und Minghui Wang. A Double Selection Approach to Achieve Specific Expression of Toxin Genes for Ovarian Cancer Gene Therapy. Fort Belvoir, VA: Defense Technical Information Center, November 2007. http://dx.doi.org/10.21236/ada485589.

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5

Curiel, David T., Gene Siegal und Minghui Wang. A Double Selection Approach to Achieve Specific Expression of Toxin Genes for Ovarian Cancer Gene Therapy. Fort Belvoir, VA: Defense Technical Information Center, November 2006. http://dx.doi.org/10.21236/ada472761.

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6

Savageau, Michael A. Selection and Computational Potential of Gene Control Elements and Their Circuitry. Fort Belvoir, VA: Defense Technical Information Center, Mai 2001. http://dx.doi.org/10.21236/ada389769.

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7

Zeng, Jian, Ali Toosi, Rohan L. Fernando, Jack C. M. Dekkers und Dorian J. Garrick. Genomic Selection of Purebred Animals for Crossbred Performance in the Presence of Dominant Gene Action. Ames (Iowa): Iowa State University, Januar 2013. http://dx.doi.org/10.31274/ans_air-180814-1249.

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8

Yeung, Ka Y., Roger E. Bumgarner und Adrian E. Raftery. Bayesian Model Averaging: Development of an Improved Multi-Class, Gene Selection and Classification Tool for Microarray Data. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2004. http://dx.doi.org/10.21236/ada454826.

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9

Kufe, Donald W. Gene Therapy of Breast Cancer: Studies of Selective Promoter/Enhancer-Modified Vectors to Deliver Suicide Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada368313.

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10

Bagamasbad, Pia. Selective Gene Regulation by Androgen Receptor in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2013. http://dx.doi.org/10.21236/ada612316.

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