Relevant bibliographies by topics / Genetic analyses

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Genetic analyses'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Genetic analyses"

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Škorput, Dubravko, Kristina Gvozdanović, Vedran Klišanić, Sven Menčik, Danijel Karolyi, Polonca Margeta, Goran Kušec, Ivona Djurkin Kušec, Zoran Luković, and Krešimir Salajpal. "Genetic diversity in Banija spotted pig: pedigree and microsatellite analyses." Journal of Central European Agriculture 19, no. 4 (2018): 871–76. http://dx.doi.org/10.5513/jcea01/19.4.2335.

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Eley, Thalia C., and Robert Plomin. "Genetic analyses of emotionality." Current Opinion in Neurobiology 7, no. 2 (April 1997): 279–84. http://dx.doi.org/10.1016/s0959-4388(97)80017-7.

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Pato, Carlos N., Kim M. Schindler, and Michele T. Pato. "Genetic analyses of schizophrenia." Current Psychiatry Reports 2, no. 2 (March 2000): 137–42. http://dx.doi.org/10.1007/s11920-000-0058-7.

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Hakonarson, Hakon, and Eva Halapi. "Genetic Analyses in Asthma." American Journal of PharmacoGenomics 2, no. 3 (2002): 155–66. http://dx.doi.org/10.2165/00129785-200202030-00001.

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Grünwald, N. J., S. E. Everhart, B. J. Knaus, and Z. N. Kamvar. "Best Practices for Population Genetic Analyses." Phytopathology® 107, no. 9 (September 2017): 1000–1010. http://dx.doi.org/10.1094/phyto-12-16-0425-rvw.

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Population genetic analysis is a powerful tool to understand how pathogens emerge and adapt. However, determining the genetic structure of populations requires complex knowledge on a range of subtle skills that are often not explicitly stated in book chapters or review articles on population genetics. What is a good sampling strategy? How many isolates should I sample? How do I include positive and negative controls in my molecular assays? What marker system should I use? This review will attempt to address many of these practical questions that are often not readily answered from reading books or reviews on the topic, but emerge from discussions with colleagues and from practical experience. A further complication for microbial or pathogen populations is the frequent observation of clonality or partial clonality. Clonality invariably makes analyses of population data difficult because many assumptions underlying the theory from which analysis methods were derived are often violated. This review provides practical guidance on how to navigate through the complex web of data analyses of pathogens that may violate typical population genetics assumptions. We also provide resources and examples for analysis in the R programming environment.
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Zhao, J., Y. Xiang, X. R. Wan, F. Z. Feng, Q. C. Cui, and X. Y. Yang. "Molecular Genetic Analyses of Choriocarcinoma." Placenta 30, no. 9 (September 2009): 816–20. http://dx.doi.org/10.1016/j.placenta.2009.06.011.

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Langaee, Taimour, and Mostafa Ronaghi. "Genetic variation analyses by Pyrosequencing." Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 573, no. 1-2 (June 2005): 96–102. http://dx.doi.org/10.1016/j.mrfmmm.2004.07.023.

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Wickstrom, S. A., K. Radovanac, and R. Fassler. "Genetic Analyses of Integrin Signaling." Cold Spring Harbor Perspectives in Biology 3, no. 2 (December 30, 2010): a005116. http://dx.doi.org/10.1101/cshperspect.a005116.

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Lauritzen, Steffen L., and Nuala A. Sheehan. "Graphical Models for Genetic Analyses." Statistical Science 18, no. 4 (November 2003): 489–514. http://dx.doi.org/10.1214/ss/1081443232.

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Pääbo, Svante, Hendrik Poinar, David Serre, Viviane Jaenicke-Després, Juliane Hebler, Nadin Rohland, Melanie Kuch, Johannes Krause, Linda Vigilant, and Michael Hofreiter. "Genetic Analyses from Ancient DNA." Annual Review of Genetics 38, no. 1 (December 2004): 645–79. http://dx.doi.org/10.1146/annurev.genet.37.110801.143214.

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Dissertations / Theses on the topic "Genetic analyses"

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Johansson, Henrik. "Microfluidic and Molecular Tools for Genetic Analyses." Doctoral thesis, Uppsala universitet, Medicinsk genetik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121536.

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Methods that enable interrogation of multiple genomic regions in parallel are very useful for efficient detection of genetic variation. Two different types of probes are described in this thesis that can be used for direct analysis or for sample preparation upstream of Next Generation Sequencing.  In addition to the development of molecular probing systems it also reports on the progress of two assay formats for biological experiments. The Selector probe enrich for genomic regions of interest by probe mediated specific circularization of target fragments. Amplification based enrichment of circles can be carried out using polymerase chain reaction, rolling-circle amplification or multiple displacement amplification. Enrichment of all exons in 28 genes known to be mutated in lung and/or colon cancer is demonstrated.  Selection and analysis by SOLiD Sequencing was performed on fresh frozen and formalin fixed paraffin embedded (FFPE) samples, and mutations previously detected by Sanger sequencing were detected.  The extractor probe is another probe variant that can be used for multiplex enrichment of DNA. It targets genomic fragments by using both ligation and sequence specific elongation for discrimination between on and off target sequences. A microfluidic platform fabricated by compact disc injection molding that can be used for biological assays is described.  Microchannel structures in thermoplastic material are coated with silicon dioxide by electron beam evaporation which facilitates closing of the structures by PDMS- glass bonding by ozone plasma. The platform’s utility for biological experiments is demonstrated by for detection of amplified single molecules (ASM), cell culturing and on-chip peristaltic pumping. The thesis also includes an exploratory study for the purpose of using a non-optical system for detection of ASM’s.  Optimizations were performed of the conditions needed in order to detect an increase in hydrodynamic size of magnetic particles, using a superconducting quantum interference device (SQUID), as they form complex with ASM’s.
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Ussar, Siegfried. "Genetic analyses of Kindlins in mice." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-100876.

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Hastings, Ian M. "Genetic and biochemical analyses of growth." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/10948.

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Neamat-Allah, Mustafa Ahmed. "The genetic analyses of diabetic nephropathy." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369913.

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Tsuchiya, Yoichi. "Application of genetic analyses to brewing." Kyoto University, 1999. http://hdl.handle.net/2433/181920.

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Ashbrook, David. "A systems-genetics analyses of complex phenotypes." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/a-systemsgenetics-analyses-of-complex-phenotypes(a3e7ad8e-b23b-40fd-821e-26a6c1a63d38).html.

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Complex phenotypes are traits which are influenced by many factors, and not just a single gene, as for classical Mendelian traits. The brain, and its resultant behaviour, gives us a large subset of complex phenotypes to examine. Variation in these traits is affected by a range of different influences, both genetic and environmental, including social interactions and the effects of parents. Systems-genetics provides us with a framework in which to examine these complex traits, seeking to connect genetic variants to the phenotypes they cause, through intermediate phenotypes, such as gene expression and protein levels. This approach has been developed to exploit and analyse massive data sets generated for example in genomics and transcriptomics. In the first half of this thesis, I combine genetic linkage data from the BXD recombinant inbred mouse panel with genome-wide association data from humans to identify novel candidate genes, and use online gene annotations and functional descriptions to support these candidates. Firstly, I discovered MGST3 as a novel regulator of hippocampus size, which may be linked to neurodegenerative disorders. Secondly, I identified that CMYA5, MCTP1, TNR and RXRG are associated with mouse anxiety-like phenotypes and human bipolar disorder, and provide evidence that MCTP1, TNR and RXRG may be acting via inter-cellular signalling in the striatum. The second half of this thesis uses different cross-fostering designs between genetically variable BXD lines and the genetically uniform C57BL/6J strain to identify indirect genetic effects and the loci underlying them. With this, I have found novel loci expressed in mothers that alter offspring behaviour, novel loci expressed in offspring affecting the level of maternal care, and novel loci expressed in offspring, which alter the behaviour of their nestmates, as well as the level of maternal care they receive. Further I provide evidence of co-adaptation between maternal and offspring genotypes, and a positive indirect genetic effect of offspring on their nestmates, supportive of a role for kin selection. Finally, I demonstrate that the BXD lines can be used to investigate genes with parent-of-origin dependent expression, which have an indirect genetic effect on maternal care. In conclusion, this thesis identifies a number of novel loci, and in some cases genes, associated with complex traits. Not only are these techniques applicable to other phenotypes and other questions, but the candidates I identify can now be examined further in vitro or in vivo.
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Cleary, Helen Julia. "Genetic analyses of radiation-induced leukaemias/lymphomas." Thesis, Brunel University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324649.

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Anderson, Carl. "Genetic analyses of age at onset traits." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/1793.

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The identification of factors underlying complex trait variation is a major goal in the field of genetics. For normally distributed, fully observed trait data there are many well established statistical methods for partitioning phenotypic variation and for mapping quantitative trait loci (QTL). Survival or time-to-event traits often follow non-normal distributions and frequently contain partially-known (or censored) trait data. If standard statistical methods are used to analyse age at onset data a bias can be introduced through a failure to account for the non-normal distribution of the data and the presence of censoring. Complex statistical methods have been developed to partition trait variation and map QTL for age at onset or survival traits. In this thesis, the use of these survival analysis methods is compared to more established statistical methods for the analysis of age-at-onset data. A brief introduction to the analysis of human variation and the issues associated with the analysis of age at onset data is given. The methods currently used to partition trait variation and map QTL for survival traits are discussed (Chapter 1). Age-specific penetrances can be used to model the age-at-onset of disease in unaffected individuals. This parametric method is used to identify loci underlying susceptibility to a novel co-morbid psychiatric phenotype (depression and unexplained swelling). The method is compared to a non-parametric variance component (VC) QTL mapping method that does not account for the age at onset of the disease. Parametric linkage analysis identified two suggestive loci, neither of which were supported by the standard variance component analysis. VC analysis identified a suggestive linkage region on chromosome 14 which decreased upon fine mapping (Chapter 2). Many of the current methods used to analyse survival data in human genetics are based on methods originally derived by animal geneticists. The analysis of survival traits in some experimental populations is simplified by the presence of fully inbred lines. However, for complex traits the methods are both computationally intensive and not widely available. A grouped linear regression method is proposed for the analysis of continuous survival data in fully inbred lines. Using simulation the method is compared to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods, is significantly better than the standard linear regression method when censored observations are present and is computationally simple (Chapter 3). A sample of 446 monozygotic (MZ) twins, 633 dizygotic (DZ) twins and 223 siblings was used to partition the inter-individual variance in age at menarche. The analysis was carried out using both a standard method which failed to account for the censored nature of the data and a mixed effects Cox model which fits a frailty model to the random effects. The standard methodology suggested that an additive genetic model best described the data. The most parsimonious model when using the frailty method included additive genetic and common environmental effects (ACE). The difference between the two models was caused by the different ascertainment of the siblings. The frailty model estimated the heritability of age at menarche to be 0.57 (Chapter 4). In Chapter 5, a sample of 2,685 pseudo-independent sib-pairs is used in a genomewide linkage scan for QTL underlying variation in age-at-menarche. The sample comprises of the adolescent sample discussed in chapter 4, and three adult cohorts. The proportion of censoring in the sample is 1.20% so a standard QTL mapping method is used. Two QTL of suggestive significance are identified on chromosomes 11p and 3p. The candidate genes WT1 and FSHB are located within the linkage peak on 11p. After the removal of bivariate outliers a locus on chromosome 12q was identified. No significant QTL were detected which suggests age-at-menarche is influenced by multiple genes of small effect. The thesis concludes with a general discussion (Chapter 6).
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Nettelblad, Carl. "Using Markov models and a stochastic Lipschitz condition for genetic analyses." Licentiate thesis, Uppsala universitet, Avdelningen för teknisk databehandling, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120295.

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A proper understanding of biological processes requires an understanding of genetics and evolutionary mechanisms. The vast amounts of genetical information that can routinely be extracted with modern technology have so far not been accompanied by an equally extended understanding of the corresponding processes. The relationship between a single gene and the resulting properties, phenotype of an individual is rarely clear. This thesis addresses several computational challenges regarding identifying and assessing the effects of quantitative trait loci (QTL), genomic positions where variation is affecting a trait. The genetic information available for each individual is rarely complete, meaning that the unknown variable of the genotype in the loci modelled also needs to be addressed. This thesis contains the presentation of new tools for employing the information that is available in a way that maximizes the information used, by using hidden Markov models (HMMs), resulting in a change in algorithm runtime complexity from exponential to log-linear, in terms of the number of markers. It also proposes the introduction of inferred haplotypes to further increase the power to assess these unknown variables for pedigrees of related genetically diverse individuals. Modelling consequences of partial genetic information are also treated. Furthermore, genes are not directly affecting traits, but are rather expressed in the environment of and in concordance with other genes. Therefore, significant interactions can be expected within genes, where some combination of genetic variation gives a pronounced, or even opposite, effect, compared to when occurring separately. This thesis addresses how to perform efficient scans for multiple interacting loci, as well as how to derive highly accurate empirical significance tests in these settings. This is done by analyzing the mathematical properties of the objective function describing the quality of model fits, and reformulating it through a simple transformation. Combined with the presented prototype of a problem-solving environment, these developments can make multi-dimensional searches for QTL routine, allowing the pursuit of new biological insight.
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Pernhorst, Katharina [Verfasser]. "Molecular genetic analyses in acquired epilepsies / Katharina Pernhorst." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1047622750/34.

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Books on the topic "Genetic analyses"

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Kamp, Andrena M. Pleiomorphic deterioration in entomopathogenic fungi: Biochemical and genetic analyses. St. Catharines, Ont: Brock University, Dept. of Biological Sciences, 2001.

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Breadon, Robert Leslie. Development of a system of genetic analyses for "Rhodomicrobium vannielii". [s.l.]: typescript, 1988.

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Tian, Jichun, Zhiying DENG, Kunpu Zhang, Haixia Yu, and Xiaoling Jiang. Genetic Analyses of Wheat and Molecular Marker-Assisted Breeding, Volume 1. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7390-4.

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Tian, Jichun, Jiansheng Chen, Guangfeng Chen, Peng Wu, Han Zhang, and Yong Zhao. Genetic Analyses of Wheat and Molecular Marker-Assisted Breeding, Volume 2. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7447-5.

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Neurotechnological, Interventions Therapy or Enhancement (Conference) (2012 Tilburg Netherlands). Beyond therapy v. enhancement?: Multidisciplinary analyses of heated debate. Pisa: Pisa University Press, 2013.

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Brown, J. G. Differentiation of walleye (Stizostedion vitreum) stocks and comparison to their pond-reared stocks using morphological and genetic analyses. Winnipeg, Man: Central and Arctic Region, Dept. of Fisheries and Oceans, 1994.

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Analysis of human genetic linkage. Baltimore: Johns Hopkins University Press, 1985.

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Analysis of human genetic linkage. Baltimore: Johns Hopkins University Press, 1991.

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Analysis of human genetic linkage. 3rd ed. Baltimore: Johns Hopkins University Press, 1999.

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Macartney, Donia P. Molecular and genetic analyses of the central control regions of the broad host range bacterial plasmids RK2 and R751. Birmingham: University of Birmingham, 1996.

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Book chapters on the topic "Genetic analyses"

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Zschocke, Johannes. "Molecular Genetic Analyses." In Inherited Metabolic Diseases, 489–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49410-3_39.

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Berry, Colin, Jason M. Meyer, Marjorie A. Hoy, John B. Heppner, William Tinzaara, Clifford S. Gold, Clifford S. Gold, et al. "Behavior: Molecular Genetic Analyses." In Encyclopedia of Entomology, 453–64. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_270.

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Kato, Naohiro. "Structural Analyses of Living Plant Nuclei." In Genetic Engineering, 65–90. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0073-5_4.

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Manly, Bryan F. J. "Further analyses using genetic data." In The Statistics of Natural Selection on Animal Populations, 362–82. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-4840-2_12.

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Neale, Michael C., and Lon R. Cardon. "The Scope of Genetic Analyses." In Methodology for Genetic Studies of Twins and Families, 1–33. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-8018-2_1.

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Benigni, R., and A. Giuliani. "Multivariate Analyses in Genetic Toxicology." In Eurocourses: Chemical and Environmental Science, 347–75. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3198-8_10.

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Connell, T. D., D. S. Barritt, W. J. Black, T. H. Kawula, D. G. Klapper, R. S. Schwalbe, A. Stephenson, and J. G. Cannon. "Genetic and biochemical analyses of protein II." In Gonococci and Meningococci, 235–38. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1383-7_38.

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Banjade, Sudeep, Shaogeng Tang, and Scott D. Emr. "Genetic and Biochemical Analyses of Yeast ESCRT." In Methods in Molecular Biology, 105–16. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9492-2_8.

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Niwa, T., and M. Tanaka. "Analyses of Simple Genetic Algorithms and Island Model Parallel Genetic Algorithms." In Artificial Neural Nets and Genetic Algorithms, 224–28. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_49.

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Auer, Sebastian, and Inés Ibañez-Tallon. "Development and Application of Membrane-Tethered Toxins for Genetic Analyses of Neuronal Circuits." In Controlled Genetic Manipulations, 141–64. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-533-6_8.

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Conference papers on the topic "Genetic analyses"

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Eftimov, Tome, and Peter Korošec. "Statistical analyses for meta-heuristic stochastic optimization algorithms." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389881.

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Eftimov, Tome, and Peter Korošec. "Statistical analyses for meta-heuristic stochastic optimization algorithms." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3461438.

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Lehre, Per Kristian, and Xiaoyu Qin. "More precise runtime analyses of non-elitist EAs in uncertain environments." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3459312.

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Doerr, Benjamin, and Weijie Zheng. "Theoretical analyses of multi-objective evolutionary algorithms on multi-modal objectives." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3462719.

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Dingiun Chen, Keith C. C. Chan, and Xindong Wu. "Gene expression analyses using Genetic Algorithm based hybrid approaches." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4630913.

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Qi, X., and F. Palmieri. "Analyses of the genetic algorithms in the continuous space." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226069.

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Riudalbas, Laia S., Sónia A. Melo, Anna Portela Mestres, and Manel Esteller. "Abstract 4887: Genetic analyses of chromatin remodelers in cancer." In Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/1538-7445.am10-4887.

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Cornils, K., L. Thielecke, D. Winkelmann, M. Lesche, T. Aranyossy, A. Dahl, I. Roeder, B. Fehse, and I. Glauche. "Analyses of clonality of BcrAbl-induced leukemia by Genetic Barcodes." In 30. Jahrestagung der Kind-Philipp-Stiftung für pädiatrisch-onkologische Forschung. Georg Thieme Verlag KG, 2017. http://dx.doi.org/10.1055/s-0037-1602198.

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Simion, Monica, Irina Kleps, Florea Craciunoiu, Lorand Savu, Mihaela Miu, and Adina Bragaru. "Development of Technology for Silicon Test Device for Genetic Analyses." In 2006 International Semiconductor Conference. IEEE, 2006. http://dx.doi.org/10.1109/smicnd.2006.283971.

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Wang, W. X. "Colony image acquisition and genetic segmentation algorithm and colony analyses." In IS&T/SPIE Electronic Imaging. SPIE, 2012. http://dx.doi.org/10.1117/12.913588.

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Reports on the topic "Genetic analyses"

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Beamer, Wesley G. Genetic and Dynamic Analyses of Murine Peak Bone Density. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada400443.

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Gutierrez, Gustavo A., Mary H. Healey, and P. Jeff Berger. Genetic Analyses of Days Open Using a Random Regression Model. Ames (Iowa): Iowa State University, January 2008. http://dx.doi.org/10.31274/ans_air-180814-613.

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Brannon, E. L., A. L. Setter, T. L. Welsh, S. J. Rocklage, G. H. Thorgaard, and S. A. Cummings. Genetic analysis of Oncorhynchus nerka. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/6973805.

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Barkan, Alice. Genetic Analysis of Chloroplast Translation. Office of Scientific and Technical Information (OSTI), August 2005. http://dx.doi.org/10.2172/842645.

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Gunsalus, Robert P. Genetic Analysis of Hyperthermophilic Archaebacterial Phenomena. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada286106.

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McAdams, Harley. Genetic Regulatory Networks: Analysis and Simulation. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada410805.

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Cahill, Gregory M. Genetic Analysis of Vertbrate Circadian Rhythmicity. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada349604.

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Freeling, M. A genetic analysis of Adhl regulation. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/6854161.

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Freeling, M. A genetic analysis of Adh1 regulation. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/5821489.

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MCCARTY D R. GENETIC ANALYSIS OF ABSCISIC ACID BIOSYNTHESIS. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1032839.

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