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Journal articles on the topic 'Genetic Function'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Sommers, Marilyn Sawyer, and Theresa Beerry. "Foundations of Genetics: Genetic Structure, Function, and Therapeutics." AACN Clinical Issues: Advanced Practice in Acute and Critical Care 9, no. 4 (November 1998): 467–82. http://dx.doi.org/10.1097/00044067-199811000-00002.

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2

Montana, David J. "Strongly Typed Genetic Programming." Evolutionary Computation 3, no. 2 (June 1995): 199–230. http://dx.doi.org/10.1162/evco.1995.3.2.199.

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Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on particular data types, this can lead to unnecessarily large search times and/or unnecessarily poor generalization performance. Strongly typed genetic programming (STGP) is an enhanced version of genetic programming that enforces data-type constraints and whose use of generic functions and generic data types makes it more powerful than other approaches to type-constraint enforcement. After describing its operation, we illustrate its use on problems in two domains, matrix/vector manipulation and list manipulation, which require its generality. The examples are (1) the multidimensional least-squares regression problem, (2) the multidimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.

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3

Hill, A., and K. Bloom. "Genetic manipulation of centromere function." Molecular and Cellular Biology 7, no. 7 (July 1987): 2397–405. http://dx.doi.org/10.1128/mcb.7.7.2397-2405.1987.

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A conditional centromere was constructed in Saccharomyces cerevisiae by placing the centromere of chromosome III immediately downstream from the inducible GAL1 promoter from S. cerevisiae. By utilizing growth conditions that favor either transcriptional induction (galactose-carbon source) or repression (glucose-carbon source) from the GAL1 promoter, centromere function can be switched off or on, respectively. With the conditional centromere we were able to radically alter the mitotic transmission pattern of both monocentric and dicentric plasmids. Moreover, it was possible to selectively induce the loss of a single chromosome from a mitotically dividing population of cells. We observed that the induction of chromosome III aneuploidy resulted in a dramatic change in cell morphology. The construction of a conditional centromere represents a novel way to create conditional mutations of cis-acting DNA elements and will be useful for further analysis of this important stabilizing element.

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4

Schulman, I. G., and K. Bloom. "Genetic dissection of centromere function." Molecular and Cellular Biology 13, no. 6 (June 1993): 3156–66. http://dx.doi.org/10.1128/mcb.13.6.3156-3166.1993.

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A system to detect a minimal function of Saccharomyces cerevisiae centromeres in vivo has been developed. Centromere DNA mutants have been examined and found to be active in a plasmid copy number control assay in the absence of segregation. The experiments allow the identification of a minimal centromere unit, CDE III, independently of its ability to mediate chromosome segregation. Centromere-mediated plasmid copy number control correlates with the ability of CDE III to assemble a DNA-protein complex. Cells forced to maintain excess copies of CDE III exhibit increased loss of a nonessential artificial chromosome. Thus, segregationally impaired centromeres can have negative effects in trans on chromosome segregation. The use of a plasmid copy number control assay has allowed assembly steps preceding chromosome segregation to be defined.

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5

JOOST, OSCAR, JEMMA B WILK, L. ADRIENNE CUPPLES, MICHAEL HARMON, AMANDA M SHEARMAN, CLINTON T BALDWIN, GEORGE T O'CONNOR, RICHARD H MYERS, and DANIEL J GOTTLIEB. "Genetic Loci Influencing Lung Function." American Journal of Respiratory and Critical Care Medicine 165, no. 6 (March 15, 2002): 795–99. http://dx.doi.org/10.1164/ajrccm.165.6.2102057.

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6

Schulman, I. G., and K. Bloom. "Genetic dissection of centromere function." Molecular and Cellular Biology 13, no. 6 (June 1993): 3156–66. http://dx.doi.org/10.1128/mcb.13.6.3156.

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A system to detect a minimal function of Saccharomyces cerevisiae centromeres in vivo has been developed. Centromere DNA mutants have been examined and found to be active in a plasmid copy number control assay in the absence of segregation. The experiments allow the identification of a minimal centromere unit, CDE III, independently of its ability to mediate chromosome segregation. Centromere-mediated plasmid copy number control correlates with the ability of CDE III to assemble a DNA-protein complex. Cells forced to maintain excess copies of CDE III exhibit increased loss of a nonessential artificial chromosome. Thus, segregationally impaired centromeres can have negative effects in trans on chromosome segregation. The use of a plasmid copy number control assay has allowed assembly steps preceding chromosome segregation to be defined.

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7

Jones, L. C. "Genetic regulation of endothelial function." Heart 91, no. 10 (October 1, 2005): 1275–77. http://dx.doi.org/10.1136/hrt.2005.061325.

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8

Hill, A., and K. Bloom. "Genetic manipulation of centromere function." Molecular and Cellular Biology 7, no. 7 (July 1987): 2397–405. http://dx.doi.org/10.1128/mcb.7.7.2397.

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A conditional centromere was constructed in Saccharomyces cerevisiae by placing the centromere of chromosome III immediately downstream from the inducible GAL1 promoter from S. cerevisiae. By utilizing growth conditions that favor either transcriptional induction (galactose-carbon source) or repression (glucose-carbon source) from the GAL1 promoter, centromere function can be switched off or on, respectively. With the conditional centromere we were able to radically alter the mitotic transmission pattern of both monocentric and dicentric plasmids. Moreover, it was possible to selectively induce the loss of a single chromosome from a mitotically dividing population of cells. We observed that the induction of chromosome III aneuploidy resulted in a dramatic change in cell morphology. The construction of a conditional centromere represents a novel way to create conditional mutations of cis-acting DNA elements and will be useful for further analysis of this important stabilizing element.

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9

Menkes, John H. "Genetic disorders of mitochondrial function." Journal of Pediatrics 110, no. 2 (February 1987): 255–59. http://dx.doi.org/10.1016/s0022-3476(87)80166-x.

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10

Szewczak, Lara. "Finding Genetic Regulators, Forecasting Function." Cell 174, no. 2 (July 2018): 247–49. http://dx.doi.org/10.1016/j.cell.2018.06.043.

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11

Plasterk, R. "Genetic switches: mechanism and function." Trends in Genetics 8, no. 1 (1992): 403–6. http://dx.doi.org/10.1016/0168-9525(92)90170-9.

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12

Plasterk, R. H. A. "Genetic switches: mechanism and function." Trends in Genetics 8, no. 12 (December 1992): 403–6. http://dx.doi.org/10.1016/0168-9525(92)90320-4.

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13

Angiolillo, Dominick J., José Luis Ferreiro, Matthew J. Price, Ajay J. Kirtane, and Gregg W. Stone. "Platelet Function and Genetic Testing." Journal of the American College of Cardiology 62, no. 17 (October 2013): S21—S31. http://dx.doi.org/10.1016/j.jacc.2013.08.704.

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14

Smith, M. Mitchell, and Maria Soledad Santisteban. "Genetic Dissection of Histone Function." Methods 15, no. 4 (August 1998): 269–81. http://dx.doi.org/10.1006/meth.1998.0631.

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15

McGuigan, Katrina, Anna Van Homrigh, and Mark W. Blows. "Genetic Analysis of Female Preference Functions as Function‐Valued Traits." American Naturalist 172, no. 2 (August 2008): 194–202. http://dx.doi.org/10.1086/588075.

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16

Keller, Mark P., Daniel M. Gatti, Kathryn L. Schueler, Mary E. Rabaglia, Donnie S. Stapleton, Petr Simecek, Matthew Vincent, et al. "Genetic Drivers of Pancreatic Islet Function." Genetics 209, no. 1 (March 22, 2018): 335–56. http://dx.doi.org/10.1534/genetics.118.300864.

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17

Izadkhah, Habib, and Mahjoubeh Tajgardan. "Information Theoretic Objective Function for Genetic Software Clustering." Proceedings 46, no. 1 (November 17, 2019): 18. http://dx.doi.org/10.3390/ecea-5-06681.

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Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

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18

Chen, Y. "Genetics and pulmonary medicine bullet 10: Genetic epidemiology of pulmonary function." Thorax 54, no. 9 (September 1, 1999): 818–24. http://dx.doi.org/10.1136/thx.54.9.818.

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19

Siddique, Imad, K. Scott Brimble, Louise Walkin, Angela Summers, Paul Brenchley, Sarah Herrick, and Peter J. Margetts. "Genetic Polymorphisms and Peritoneal Membrane Function." Peritoneal Dialysis International: Journal of the International Society for Peritoneal Dialysis 35, no. 5 (September 2015): 517–29. http://dx.doi.org/10.3747/pdi.2014.00049.

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BackgroundOutcomes for peritoneal dialysis (PD) patients are affected by the characteristics of the peritoneal membrane, which may be determined by genetic variants. We carried out a systematic review of the literature to identify studies which assessed the association between genetic polymorphisms, peritoneal membrane solute transport, and clinical outcomes for PD patients.MethodsThe National Library of Medicine was searched using a variety of strategies. Studies which met our inclusion criteria were reviewed and data abstracted. Our outcomes of interest included: high transport status peritoneal membrane, risk for peritonitis, encapsulating peritoneal sclerosis (EPS), patient and technique survival. We combined data from studies which evaluated the same genetic polymorphism and the same outcome.ResultsWe evaluated 18 relevant studies. All studies used a candidate gene approach. Gene polymorphisms in the interleukin (IL)-6 gene were associated with peritoneal membrane solute transport in several studies in different ethnic populations. Associations with solute transport and polymorphisms in endothelial nitric oxide synthase and receptor for advanced glycation end product genes were also identified. There was evidence of a genetic predisposition for peritonitis found in 2 studies, and for EPS in 1 study. Survival was found to be associated with a polymorphism in vascular endothelial growth factor and technique failure was associated with a polymorphism in the IL-1 receptor antagonist.ConclusionsThere is evidence that characteristics of the peritoneal membrane and clinical outcomes for PD patients have genetic determinants. The most consistent association was between IL-6 gene polymorphisms and peritoneal membrane solute transport.

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20

Ramaswami, Mani, Sujata Rao, Alexander Van Der Bliek, Regis B. Kelly, and K. S. Krishnan. "Genetic Studies on Dynamin Function inDrosophila." Journal of Neurogenetics 9, no. 2 (January 1993): 73–87. http://dx.doi.org/10.3109/01677069309083451.

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21

Someya, Hiroshi, and Masayuki Yamamura. "A Genetic Algorithm for Function Optimization." IEEJ Transactions on Electronics, Information and Systems 122, no. 3 (2002): 363–73. http://dx.doi.org/10.1541/ieejeiss1987.122.3_363.

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22

Dugda, M. T., A. T. Workineh, A. Homaifar, and J. Hyoun Kim. "Receiver Function Inversion Using Genetic Algorithms." Bulletin of the Seismological Society of America 102, no. 5 (October 1, 2012): 2245–51. http://dx.doi.org/10.1785/0120120001.

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23

Wolf, Matthew J., and Howard A. Rockman. "Drosophila, Genetic Screens, and Cardiac Function." Circulation Research 109, no. 7 (September 16, 2011): 794–806. http://dx.doi.org/10.1161/circresaha.111.244897.

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24

Reichardt, H., F. Tronche, C. Kellendonk, T. Mantamadiotis, and G. Schütz. "GENETIC DISSECTION OF GLUCOCORTICOID RECEPTOR FUNCTION." Biochemical Society Transactions 27, no. 1 (February 1, 1999): A6. http://dx.doi.org/10.1042/bst027a006.

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25

Howell, M. N., T. J. Gordon, and F. V. Brandao. "Genetic learning automata for function optimization." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 6 (December 2002): 804–15. http://dx.doi.org/10.1109/tsmcb.2002.1049614.

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26

Kunicki, Thomas J., Shirley A. Williams, and Diane J. Nugent. "Genetic variants that affect platelet function." Current Opinion in Hematology 19, no. 5 (September 2012): 371–79. http://dx.doi.org/10.1097/moh.0b013e3283567526.

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27

Mizuta, R. "Molecular genetic characterization of XRCC4 function." International Immunology 9, no. 10 (October 1, 1997): 1607–13. http://dx.doi.org/10.1093/intimm/9.10.1607.

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28

Niekum, Scott, Andrew G. Barto, and Lee Spector. "Genetic Programming for Reward Function Search." IEEE Transactions on Autonomous Mental Development 2, no. 2 (June 2010): 83–90. http://dx.doi.org/10.1109/tamd.2010.2051436.

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29

Kumar, Rajesh, Max A. Seibold, Melinda C. Aldrich, L. Keoki Williams, Alex P. Reiner, Laura Colangelo, Joshua Galanter, et al. "Genetic Ancestry in Lung-Function Predictions." New England Journal of Medicine 363, no. 4 (July 22, 2010): 321–30. http://dx.doi.org/10.1056/nejmoa0907897.

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30

O'Connor, Michael, Carleen A. Brunelli, Matthew A. Firpo, Steven T. Gregory, Kathy R. Lieberman, J. Stephen Lodmell, Hervé Moine, Donald I. Van Ryk, and Albert E. Dahlberg. "Genetic probes of ribosomal RNA function." Biochemistry and Cell Biology 73, no. 11-12 (December 1, 1995): 859–68. http://dx.doi.org/10.1139/o95-093.

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We have used a genetic approach to uncover the functional roles of rRNA in protein synthesis. Mutations were constructed in a cloned rrn operon by site-directed mutagenesis or isolated by genetic selections following random mutagenesis. We have identified mutations that affect each step in the process of translation. The data are consistent with the results of biochemical and phylogenetic analyses but, in addition, have provided novel information on regions of rRNA not previously investigated.Key words: decoding, peptidyltransferase, streptomycin, paromomycin, suppression, 4.5S RNA.

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31

Tan, H. "Genetic control of sodium channel function." Cardiovascular Research 57, no. 4 (March 15, 2003): 961–73. http://dx.doi.org/10.1016/s0008-6363(02)00714-9.

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32

Cole, F. Sessions, Aaron Hamvas, and Lawrence M. Nogee. "Genetic Disorders of Neonatal Respiratory Function." Pediatric Research 50, no. 2 (August 2001): 157–62. http://dx.doi.org/10.1203/00006450-200108000-00001.

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33

Gromoll, Jörg, and Manuela Simoni. "Genetic complexity of FSH receptor function." Trends in Endocrinology & Metabolism 16, no. 8 (October 2005): 368–73. http://dx.doi.org/10.1016/j.tem.2005.05.011.

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34

Vinod, K. K. "Kosambi and the genetic mapping function." Resonance 16, no. 6 (June 2011): 540–50. http://dx.doi.org/10.1007/s12045-011-0060-x.

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35

Kim-Hellmuth, Sarah, and Tuuli Lappalainen. "Concerted Genetic Function in Blood Traits." Cell 167, no. 5 (November 2016): 1167–69. http://dx.doi.org/10.1016/j.cell.2016.10.055.

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36

Rybicki, Benjamin A., Terri H. Beaty, and Bernice H. Cohen. "Major genetic mechanisms in pulmonary function." Journal of Clinical Epidemiology 43, no. 7 (January 1990): 667–75. http://dx.doi.org/10.1016/0895-4356(90)90037-p.

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37

Jang, Kerry L., Philip A. Vernon, and W. John Livesley. "Behavioural-Genetic Perspectives on Personality Function." Canadian Journal of Psychiatry 46, no. 3 (April 2001): 234–44. http://dx.doi.org/10.1177/070674370104600303.

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38

Dreyer, M., and H. W. Rüdiger. "Genetic defects of human receptor function." Trends in Pharmacological Sciences 9, no. 3 (March 1988): 98–102. http://dx.doi.org/10.1016/0165-6147(88)90176-9.

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39

Valadan Zoej, M. J., M. Mokhtarzade, A. Mansourian, H. Ebadi, and S. Sadeghian. "Rational function optimization using genetic algorithms." International Journal of Applied Earth Observation and Geoinformation 9, no. 4 (December 2007): 403–13. http://dx.doi.org/10.1016/j.jag.2007.02.002.

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40

Lira, Sergio A. "Genetic approaches to study chemokine function." Journal of Leukocyte Biology 59, no. 1 (January 1996): 45–52. http://dx.doi.org/10.1002/jlb.59.1.45.

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41

Pultz, M. A., G. S. Carson, and B. S. Baker. "A genetic analysis of hermaphrodite, a pleiotropic sex determination gene in Drosophila melanogaster." Genetics 136, no. 1 (January 1, 1994): 195–207. http://dx.doi.org/10.1093/genetics/136.1.195.

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Abstract Sex determination in Drosophila is controlled by a cascade of regulatory genes. Here we describe hermaphrodite (her), a new component of this regulatory cascade with pleiotropic zygotic and maternal functions. Zygotically, her+ function is required for female sexual differentiation: when zygotic her+ function is lacking, females are transformed to intersexes. Zygotic her+ function may also play a role in male sexual differentiation. Maternally, her+ function is needed to ensure the viability of female progeny: a partial loss of her+ function preferentially kills daughters. In addition, her has both zygotic and maternal functions required for viability in both sexes. Temperature sensitivity prevails for all known her alleles and for all of the her phenotypes described above, suggesting that her may participate in an intrinsically temperature-sensitive process. This analysis of four her alleles also indicates that the zygotic and maternal components of of her function are differentially mutable. We have localized her cytologically to 36A3-36A11.

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42

Marín, Oscar, and Joseph G. Gleeson. "Function follows form: understanding brain function from a genetic perspective." Current Opinion in Genetics & Development 21, no. 3 (June 2011): 237–39. http://dx.doi.org/10.1016/j.gde.2011.04.007.

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43

Zhansi, Jiang, Jiang Yulong, Ma Liquan, and Feng Jianguo. "A Constrained Genetic Algorithm Based on Constraint Handling with KS Function and Grouping Penalty." International Journal of Engineering Research 4, no. 1 (January 1, 2015): 40–46. http://dx.doi.org/10.17950/ijer/v4s1/110.

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44

Yang, Mo-Hua, and Yong-Bi Fu. "AveDissR: An R Function for Assessing Genetic Distinctness and Genetic Redundancy." Applications in Plant Sciences 5, no. 7 (July 2017): 1700018. http://dx.doi.org/10.3732/apps.1700018.

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45

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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46

Dias, Rodrigo Gonçalves, Márcia Maria Gowdak, and Alexandre Costa Pereira. "Genetics and cardiovascular system: influence of human genetic variants on vascular function." Genes & Nutrition 6, no. 1 (November 3, 2010): 55–62. http://dx.doi.org/10.1007/s12263-010-0193-7.

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47

Foss, E., R. Lande, F. W. Stahl, and C. M. Steinberg. "Chiasma interference as a function of genetic distance." Genetics 133, no. 3 (March 1, 1993): 681–91. http://dx.doi.org/10.1093/genetics/133.3.681.

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Abstract For many organisms, meiotic double crossing over is less frequent than expected on the assumption that exchanges occur at random with respect to each other. This "interference," which can be almost total for nearby intervals, diminishes as the intervals in which the double crossovers are scored are moved farther apart. Most models for interference have assumed, at least implicitly, that the intensity of interference depends inversely on the physical distance separating the intervals. However, several observations suggest that interference depends on genetic distance (Morgans) rather than physical distance (base pairs or micrometers). Accordingly, we devise a model in which interference is related directly to genetic distance. Its central feature is that recombinational intermediates (C's) have two fates--they can be resolved with crossing over (Cx) or without (Co). We suppose that C's are distributed at random with respect to each other (no interference); interference results from constraints on the resolution of C's. The basic constraint is that each pair of neighboring Cx's must have between them a certain number of Co's. The required number of intervening Co's for a given organism or chromosome is estimated from the fraction of gene conversions that are unaccompanied by crossover of flanking markers. The predictions of the model are compared with data from Drosophila and Neurospora.

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48

Foss, E., R. Lande, F. W. Stahl, and C. M. Steinberg. "Chiasma Interference as a Function of Genetic Distance." Genetics 134, no. 3 (July 1, 1993): 997. http://dx.doi.org/10.1093/genetics/134.3.997.

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49

Kowaliw, Taras, and René Doursat. "Bias-variance decomposition in Genetic Programming." Open Mathematics 14, no. 1 (January 1, 2016): 62–80. http://dx.doi.org/10.1515/math-2016-0005.

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AbstractWe study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the overall error and its decomposed contributions. These parameters are the maximum program size, the initial population, and the function set used. We confirm and quantify several insights into the practical usage of GP, most notably that (a) the variance between runs is primarily due to initialization rather than the selection of training samples, (b) parameters can be reasonably optimized to obtain gains in efficacy, and (c) functions detrimental to evolvability are easily eliminated, while functions well-suited to the problem can greatly improve performance—therefore, larger and more diverse function sets are always preferable.

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50

Farré, Xavier, Roderic Espín, Alexandra Baiges, Eline Blommaert, Wonji Kim, Krinio Giannikou, Carmen Herranz, et al. "Evidence for shared genetic risk factors between lymphangioleiomyomatosis and pulmonary function." ERJ Open Research 8, no. 1 (October 28, 2021): 00375–2021. http://dx.doi.org/10.1183/23120541.00375-2021.

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IntroductionLymphangioleiomyomatosis (LAM) is a rare low-grade metastasising disease characterised by cystic lung destruction. The genetic basis of LAM remains incompletely determined, and the disease cell-of-origin is uncertain. We analysed the possibility of a shared genetic basis between LAM and cancer, and LAM and pulmonary function.MethodsThe results of genome-wide association studies of LAM, 17 cancer types and spirometry measures (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC ratio and peak expiratory flow (PEF)) were analysed for genetic correlations, shared genetic variants and causality. Genomic and transcriptomic data were examined, and immunodetection assays were performed to evaluate pleiotropic genes.ResultsThere were no significant overall genetic correlations between LAM and cancer, but LAM correlated negatively with FVC and PEF, and a trend in the same direction was observed for FEV1. 22 shared genetic variants were uncovered between LAM and pulmonary function, while seven shared variants were identified between LAM and cancer. The LAM-pulmonary function shared genetics identified four pleiotropic genes previously recognised in LAM single-cell transcriptomes: ADAM12, BNC2, NR2F2 and SP5. We had previously associated NR2F2 variants with LAM, and we identified its functional partner NR3C1 as another pleotropic factor. NR3C1 expression was confirmed in LAM lung lesions. Another candidate pleiotropic factor, CNTN2, was found more abundant in plasma of LAM patients than that of healthy women.ConclusionsThis study suggests the existence of a common genetic aetiology between LAM and pulmonary function.

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