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Journal articles on the topic 'Genetic architecture'

To see the other types of publications on this topic, follow the link: Genetic architecture.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Bhandare, Ashray, and Devinder Kaur. "Designing Convolutional Neural Network Architecture Using Genetic Algorithms." International Journal of Advanced Network, Monitoring and Controls 6, no. 3 (January 1, 2021): 26–35. http://dx.doi.org/10.21307/ijanmc-2021-024.

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Abstract In this paper, genetic algorithm (GA) is used to optimally determine the architecture of a convolutional neural network (CNN) that is used to classify handwritten numbers. The CNN is a class of deep feed-forward network, which have seen major success in the field of visual image analysis. During training, a good CNN architecture is capable of extracting complex features from the given training data; however, at present, there is no standard way to determine the architecture of a CNN. Domain knowledge and human expertise are required in order to design a CNN architecture. Typically architectures, The GA determine the exact architecture of a CNN by evolving the various hyper parameters of the architecture for a given application. The proposed method was tested on the MNIST dataset. The results show that the genetic algorithm is capable of generating successful CNN architectures. The proposed method performs the entire process of architecture generation without any human intervention.

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2

Nivethitha, V., and P. M Abhinaya. "Combinatorics based problem specific software architecture formulation using multi-objective genetic algorithm." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 79. http://dx.doi.org/10.14419/ijet.v7i1.7.9579.

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In Software Development Process, the design of complex systems is an important phase where software architects have to deal with abstract artefacts, procedures and ideas to discover the most suitable underlying architecture. Due to uncontrolled modifications of the design and frequent change of requirements, many of the working systems do not have a proper architecture. Most of the approaches recover the architectural blocks at the end of the development process which are not appropriate to the system considered. In order to structure these systems software components compositions and interactions should be properly adjusted which is a tedious work. Search-based Software Engineering (SBSE) is an emerging area which can support the decision making process of formulating the software architecture from initial analysis models. Thus component-based architectures is articulated as a multiple optimisation problem using evolutionary algorithms. Totally different metrics is applied looking on the design needs and also the specific domain. Thus during this analysis work, an effort has been created to propose a multi objective evolutionary approach for the invention of the underlying software system architectures beside a versatile encoding structure, correct style metrics for the fitness operate to enhance the standard and accuracy of the software system design.

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3

LICHTERMANN, DIRK, JESPER EKELUND, LEENA PELTONEN, and MARJO-RIITTA JÄRVELIN. "Genetic Architecture of Temperament." American Journal of Psychiatry 158, no. 8 (August 2001): 1339. http://dx.doi.org/10.1176/appi.ajp.158.8.1339.

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4

Moreno, G. "Genetic Architecture, Genetic Behavior, and Character Evolution." Annual Review of Ecology and Systematics 25, no. 1 (November 1994): 31–44. http://dx.doi.org/10.1146/annurev.es.25.110194.000335.

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5

Agibalov, Oleg, and Nikolay Ventsov. "On the issue of fuzzy timing estimations of the algorithms running at GPU and CPU architectures." E3S Web of Conferences 135 (2019): 01082. http://dx.doi.org/10.1051/e3sconf/201913501082.

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We consider the task of comparing fuzzy estimates of the execution parameters of genetic algorithms implemented at GPU (graphics processing unit’ GPU) and CPU (central processing unit) architectures. Fuzzy estimates are calculated based on the averaged dependencies of the genetic algorithms running time at GPU and CPU architectures from the number of individuals in the populations processed by the algorithm. The analysis of the averaged dependences of the genetic algorithms running time at GPU and CPU-architectures showed that it is possible to process 10’000 chromosomes at GPU-architecture or 5’000 chromosomes at CPUarchitecture by genetic algorithm in approximately 2’500 ms. The following is correct for the cases under consideration: “Genetic algorithms (GA) are performed in approximately 2, 500 ms (on average), ” and a sections of fuzzy sets, with a = 0.5, correspond to the intervals [2, 000.2399] for GA performed at the GPU-architecture, and [1, 400.1799] for GA performed at the CPU-architecture. Thereby, it can be said that in this case, the actual execution time of the algorithm at the GPU architecture deviates in a lesser extent from the average value than at the CPU.

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6

Rajon, Etienne, and Joshua B. Plotkin. "The evolution of genetic architectures underlying quantitative traits." Proceedings of the Royal Society B: Biological Sciences 280, no. 1769 (October 22, 2013): 20131552. http://dx.doi.org/10.1098/rspb.2013.1552.

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In the classic view introduced by R. A. Fisher, a quantitative trait is encoded by many loci with small, additive effects. Recent advances in quantitative trait loci mapping have begun to elucidate the genetic architectures underlying vast numbers of phenotypes across diverse taxa, producing observations that sometimes contrast with Fisher's blueprint. Despite these considerable empirical efforts to map the genetic determinants of traits, it remains poorly understood how the genetic architecture of a trait should evolve, or how it depends on the selection pressures on the trait. Here, we develop a simple, population-genetic model for the evolution of genetic architectures. Our model predicts that traits under moderate selection should be encoded by many loci with highly variable effects, whereas traits under either weak or strong selection should be encoded by relatively few loci. We compare these theoretical predictions with qualitative trends in the genetics of human traits, and with systematic data on the genetics of gene expression levels in yeast. Our analysis provides an evolutionary explanation for broad empirical patterns in the genetic basis for traits, and it introduces a single framework that unifies the diversity of observed genetic architectures, ranging from Mendelian to Fisherian.

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7

Lam, Max, Chia-Yen Chen, Tian Ge, Hailiang Huang, Heiko Runz, and Todd Lencz. "Dissecting the Genetic Architecture of Psychopathology With Cognitive Genetics." Biological Psychiatry 89, no. 9 (May 2021): S44—S45. http://dx.doi.org/10.1016/j.biopsych.2021.02.128.

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8

ZENG, ZHAO-BANG, CHEN-HUNG KAO, and CHRISTOPHER J. BASTEN. "Estimating the genetic architecture of quantitative traits." Genetical Research 74, no. 3 (December 1999): 279–89. http://dx.doi.org/10.1017/s0016672399004255.

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Understanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.

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9

Latifi, Mohammad, Mohammad Javad Mahdavinezhad, and Darab Diba. "UNDERSTANDING GENETIC ALGORITHMS IN ARCHITECTURE." TURKISH ONLINE JOURNAL OF DESIGN, ART AND COMMUNICATION 6, AGSE (August 10, 2016): 1385–400. http://dx.doi.org/10.7456/1060agse/023.

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10

Pierce, Khadija Robin. "Comparative Architecture of Genetic Privacy." Indiana International & Comparative Law Review 19, no. 1 (January 1, 2009): 89–128. http://dx.doi.org/10.18060/17600.

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11

Traylor, Matthew, Steve Bevan, Jean-Claude Baron, Ahamad Hassan, Cathryn M. Lewis, and Hugh S. Markus. "Genetic Architecture of Lacunar Stroke." Stroke 46, no. 9 (September 2015): 2407–12. http://dx.doi.org/10.1161/strokeaha.115.009485.

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12

Roberts, Genevieve H. L., Stephanie A. Santorico, and Richard A. Spritz. "The genetic architecture of vitiligo." Pigment Cell & Melanoma Research 33, no. 1 (December 4, 2019): 8–15. http://dx.doi.org/10.1111/pcmr.12848.

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13

Le Hellard, Stephanie, and Vidar M. Steen. "Genetic architecture of cognitive traits." Scandinavian Journal of Psychology 55, no. 3 (March 8, 2014): 255–62. http://dx.doi.org/10.1111/sjop.12112.

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14

Wang, Bing, Steven M. Smith, and Jiayang Li. "Genetic Regulation of Shoot Architecture." Annual Review of Plant Biology 69, no. 1 (April 29, 2018): 437–68. http://dx.doi.org/10.1146/annurev-arplant-042817-040422.

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15

Zahn, Laura M. "Genetic architecture of developmental disorders." Science 362, no. 6419 (December 6, 2018): 1124.3–1124. http://dx.doi.org/10.1126/science.362.6419.1124-c.

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16

Bearden, Carrie E., Katherine H. Karlsgodt, Peter Bachman, Theo G. M. van Erp, Anderson M. Winkler, and David C. Glahn. "Genetic Architecture of Declarative Memory." Neuroscientist 18, no. 5 (August 10, 2011): 516–32. http://dx.doi.org/10.1177/1073858411415113.

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17

Timpson, Nicholas J., Celia M. T. Greenwood, Nicole Soranzo, Daniel J. Lawson, and J. Brent Richards. "Heritable contributions versus genetic architecture." Nature Reviews Genetics 19, no. 3 (March 2018): 185. http://dx.doi.org/10.1038/nrg.2018.7.

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18

Utermann, G., M. Ogorelkova, and H. G. Kraft. "Genetic architecture of lipoprotein(a)." Atherosclerosis 144 (May 1999): 80. http://dx.doi.org/10.1016/s0021-9150(99)80303-7.

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19

Clarimon, Jordi, Sonia Moreno-Grau, Laura Cervera-Carles, Oriol Dols-Icardo, Pascual Sánchez-Juan, and Agustín Ruiz. "Genetic architecture of neurodegenerative dementias." Neuropharmacology 168 (May 2020): 108014. http://dx.doi.org/10.1016/j.neuropharm.2020.108014.

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20

Hansen, Thomas F. "The Evolution of Genetic Architecture." Annual Review of Ecology, Evolution, and Systematics 37, no. 1 (December 2006): 123–57. http://dx.doi.org/10.1146/annurev.ecolsys.37.091305.110224.

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21

Young, Nevin Dale. "The genetic architecture of resistance." Current Opinion in Plant Biology 3, no. 4 (August 2000): 285–90. http://dx.doi.org/10.1016/s1369-5266(00)00081-9.

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22

Peters, Ulrike, Stephanie Bien, and Niha Zubair. "Genetic architecture of colorectal cancer." Gut 64, no. 10 (July 17, 2015): 1623–36. http://dx.doi.org/10.1136/gutjnl-2013-306705.

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23

Neuner, Sarah M., Julia TCW, and Alison M. Goate. "Genetic architecture of Alzheimer's disease." Neurobiology of Disease 143 (September 2020): 104976. http://dx.doi.org/10.1016/j.nbd.2020.104976.

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24

Shatunov, Aleksey, and Ammar Al-Chalabi. "The genetic architecture of ALS." Neurobiology of Disease 147 (January 2021): 105156. http://dx.doi.org/10.1016/j.nbd.2020.105156.

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25

Lichtenberger, R., M. A. Simpson, C. Smith, J. Barker, and A. A. Navarini. "Genetic architecture of acne vulgaris." Journal of the European Academy of Dermatology and Venereology 31, no. 12 (September 24, 2017): 1978–90. http://dx.doi.org/10.1111/jdv.14385.

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26

Gallego-Martinez, Alvaro, and Jose A. Lopez-Escamez. "Genetic architecture of Meniere’s disease." Hearing Research 397 (November 2020): 107872. http://dx.doi.org/10.1016/j.heares.2019.107872.

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27

Golzio, Christelle, and Nicholas Katsanis. "Genetic architecture of reciprocal CNVs." Current Opinion in Genetics & Development 23, no. 3 (June 2013): 240–48. http://dx.doi.org/10.1016/j.gde.2013.04.013.

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28

Hershberger, Ray E., Jason Cowan, Elizabeth Jordan, and Daniel D. Kinnamon. "The Complex and Diverse Genetic Architecture of Dilated Cardiomyopathy." Circulation Research 128, no. 10 (May 14, 2021): 1514–32. http://dx.doi.org/10.1161/circresaha.121.318157.

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Our insight into the diverse and complex nature of dilated cardiomyopathy (DCM) genetic architecture continues to evolve rapidly. The foundations of DCM genetics rest on marked locus and allelic heterogeneity. While DCM exhibits a Mendelian, monogenic architecture in some families, preliminary data from our studies and others suggests that at least 20% to 30% of DCM may have an oligogenic basis, meaning that multiple rare variants from different, unlinked loci, determine the DCM phenotype. It is also likely that low-frequency and common genetic variation contribute to DCM complexity, but neither has been examined within a rare variant context. Other types of genetic variation are also likely relevant for DCM, along with gene-by-environment interaction, now established for alcohol- and chemotherapy-related DCM. Collectively, this suggests that the genetic architecture of DCM is broader in scope and more complex than previously understood. All of this elevates the impact of DCM genetics research, as greater insight into the causes of DCM can lead to interventions to mitigate or even prevent it and thus avoid the morbid and mortal scourge of human heart failure.

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29

Biton, Anne, Nicolas Traut, Jean-Baptiste Poline, Benjamin S. Aribisala, Mark E. Bastin, Robin Bülow, Simon R. Cox, et al. "Polygenic Architecture of Human Neuroanatomical Diversity." Cerebral Cortex 30, no. 4 (February 28, 2020): 2307–20. http://dx.doi.org/10.1093/cercor/bhz241.

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Abstract We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.

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30

Christians, Julian K., and Peter D. Keightley. "Genetic Architecture: Dissecting the Genetic Basis of Phenotypic Variation." Current Biology 12, no. 12 (June 2002): R415—R416. http://dx.doi.org/10.1016/s0960-9822(02)00911-9.

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31

Peiffer, Jason A., Maria C. Romay, Michael A. Gore, Sherry A. Flint-Garcia, Zhiwu Zhang, Mark J. Millard, Candice A. C. Gardner, et al. "The Genetic Architecture Of Maize Height." Genetics 196, no. 4 (February 10, 2014): 1337–56. http://dx.doi.org/10.1534/genetics.113.159152.

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32

Pollak, Martin R., and David J. Friedman. "The Genetic Architecture of Kidney Disease." Clinical Journal of the American Society of Nephrology 15, no. 2 (January 28, 2020): 268–75. http://dx.doi.org/10.2215/cjn.09340819.

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The kidney is subject to a wide range of abnormalities, many of which have a significant hereditable component. Next generation sequencing is increasingly bringing the genetic drivers of Mendelian disease into focus at the base pair level, whereas inexpensive genotyping arrays have surveyed hundreds of thousands of individuals to identify common variants that predispose to kidney dysfunction. In this first article in a CJASN series on kidney genomics, we review how both rare and common variants contribute to kidney disease, explore how evolution may influence the genetic variants that affect kidney function, consider how genetic information is and will be used in the clinic, and identify some of the most important future directions for kidney disease research. Forthcoming articles in the series will elaborate on many of these themes.

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33

Scorza, Ralph. "GENETIC MANIPULATION OF TREE FRUIT ARCHITECTURE." HortScience 25, no. 9 (September 1990): 1177d—1177. http://dx.doi.org/10.21273/hortsci.25.9.1177d.

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The genetically available range in tree fruit architecture has not been fully utilized for tree fruit breeding or production. Higher planting densities, new training systems, high coats of pruning, the need to eliminate ladders in the orchard, and mechanized harvesting require a re-evaluation of tree architecture. Dwarf, semidwarf, columnar, and spur-type trees may be more efficient than standard tree forms, especially when combined with specific production systems. Studies of the growth of novel tree types and elucidation of the inheritance of growth habit components may allow breeders to combine canopy growth characteristics to produce trees tailored to evolving production systems.

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34

Chaudhary, Rishabh, Vipul Agarwal, Mujeeba Rehman, Arjun Singh Kaushik, and Vikas Mishra. "Genetic architecture of motor neuron diseases." Journal of the Neurological Sciences 434 (March 2022): 120099. http://dx.doi.org/10.1016/j.jns.2021.120099.

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35

Ware, Stephanie M., Surbhi Bhatnagar, Phillip J. Dexheimer, James D. Wilkinson, Arthi Sridhar, Xiao Fan, Yufeng Shen, et al. "The genetic architecture of pediatric cardiomyopathy." American Journal of Human Genetics 109, no. 2 (February 2022): 282–98. http://dx.doi.org/10.1016/j.ajhg.2021.12.006.

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36

Rost, Natalia S., Steven M. Greenberg, and Jonathan Rosand. "The Genetic Architecture of Intracerebral Hemorrhage." Stroke 39, no. 7 (July 2008): 2166–73. http://dx.doi.org/10.1161/strokeaha.107.501650.

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37

Girgis, Hany S., Alison K. Hottes, and Saeed Tavazoie. "Genetic Architecture of Intrinsic Antibiotic Susceptibility." PLoS ONE 4, no. 5 (May 20, 2009): e5629. http://dx.doi.org/10.1371/journal.pone.0005629.

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38

Gao, Kate. "The genetic architecture of multiple sclerosis." Nature Medicine 25, no. 11 (November 2019): 1647. http://dx.doi.org/10.1038/s41591-019-0656-3.

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39

Menzel, Stephan, and Swee Lay Thein. "Genetic architecture of hemoglobin F control." Current Opinion in Hematology 16, no. 3 (May 2009): 179–86. http://dx.doi.org/10.1097/moh.0b013e328329d07a.

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40

Carney, Ellen F. "The genetic architecture of blood pressure." Nature Reviews Nephrology 15, no. 4 (January 22, 2019): 192. http://dx.doi.org/10.1038/s41581-019-0117-8.

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41

Munafò, Marcus R., and Jonathan Flint. "The genetic architecture of psychophysiological phenotypes." Psychophysiology 51, no. 12 (November 11, 2014): 1331–32. http://dx.doi.org/10.1111/psyp.12355.

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42

Kingwell, Katie. "Surveying the genetic architecture of MS." Nature Reviews Neurology 7, no. 10 (September 20, 2011): 535. http://dx.doi.org/10.1038/nrneurol.2011.145.

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43

Borghero, Giuseppe, Maura Pugliatti, Francesco Marrosu, Maria Giovanna Marrosu, Maria Rita Murru, Gianluca Floris, Antonino Cannas, et al. "Genetic architecture of ALS in Sardinia." Neurobiology of Aging 35, no. 12 (December 2014): 2882.e7–2882.e12. http://dx.doi.org/10.1016/j.neurobiolaging.2014.07.012.

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44

Blauwendraat, Cornelis, Mike A. Nalls, and Andrew B. Singleton. "The genetic architecture of Parkinson's disease." Lancet Neurology 19, no. 2 (February 2020): 170–78. http://dx.doi.org/10.1016/s1474-4422(19)30287-x.

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45

Mitchell, K. J., and D. J. Porteous. "Rethinking the genetic architecture of schizophrenia." Psychological Medicine 41, no. 1 (April 12, 2010): 19–32. http://dx.doi.org/10.1017/s003329171000070x.

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BackgroundFor many years, the prevailing paradigm has stated that in each individual with schizophrenia (SZ) the genetic risk is due to a combination of many genetic variants, individually of small effect. Recent empirical data are prompting a re-evaluation of this polygenic, common disease–common variant (CDCV) model. Evidence includes a lack of the expected strong positive findings from genome-wide association studies and the concurrent discovery of many different mutations that individually strongly predispose to SZ and other psychiatric disorders. This has led some to adopt a mixed model wherein some cases are caused by polygenic mechanisms and some by single mutations. This model runs counter to a substantial body of theoretical literature that had supposedly conclusively rejected Mendelian inheritance with genetic heterogeneity. Here we ask how this discrepancy between theory and data arose and propose a rationalization of the recent evidence base.MethodIn light of recent empirical findings, we reconsider the methods and conclusions of early theoretical analyses and the explicit assumptions underlying them.ResultsWe show that many of these assumptions can now be seen to be false and that the model of genetic heterogeneity is consistent with observed familial recurrence risks, endophenotype studies and other population-wide parameters.ConclusionsWe argue for a more biologically consilient mixed model that involves interactions between disease-causing and disease-modifying variants in each individual. We consider the implications of this model for moving SZ research beyond statistical associations to pathogenic mechanisms.

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46

Mitchell, Kevin J., and David J. Porteous. "RETHINKING THE GENETIC ARCHITECTURE OF SCHIZOPHRENIA." Schizophrenia Research 117, no. 2-3 (April 2010): 222. http://dx.doi.org/10.1016/j.schres.2010.02.318.

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47

Tang, K. S., Y. C. Ho, and K. F. Man. "Realizable Architecture for Genetic Algorithms Parallelism." IFAC Proceedings Volumes 30, no. 3 (April 1997): 233–38. http://dx.doi.org/10.1016/s1474-6670(17)44496-x.

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48

Claringbould, Annique, Niek de Klein, and Lude Franke. "The genetic architecture of molecular traits." Current Opinion in Systems Biology 1 (February 2017): 25–31. http://dx.doi.org/10.1016/j.coisb.2017.01.002.

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49

Hara, Kazuo, Nobuhiro Shojima, Jun Hosoe, and Takashi Kadowaki. "Genetic architecture of type 2 diabetes." Biochemical and Biophysical Research Communications 452, no. 2 (September 2014): 213–20. http://dx.doi.org/10.1016/j.bbrc.2014.08.012.

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50

Morgan, Gareth J., Brian A. Walker, and Faith E. Davies. "The genetic architecture of multiple myeloma." Nature Reviews Cancer 12, no. 5 (April 12, 2012): 335–48. http://dx.doi.org/10.1038/nrc3257.

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