Relevant bibliographies by topics / Genetics – Statistical methods

Academic literature on the topic 'Genetics – Statistical methods'

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Published: 4 June 2021
Last updated: 28 July 2024

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Journal articles on the topic "Genetics – Statistical methods"

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Montana, G. "Statistical methods in genetics." Briefings in Bioinformatics 7, no. 3 (May 23, 2006): 297–308. http://dx.doi.org/10.1093/bib/bbl028.

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Fisher, R. "Statistical methods in genetics." International Journal of Epidemiology 39, no. 2 (February 22, 2010): 329–35. http://dx.doi.org/10.1093/ije/dyp379.

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Edwards, A. W. F. "Statistical Methods for Evolutionary Trees." Genetics 183, no. 1 (September 2009): 5–12. http://dx.doi.org/10.1534/genetics.109.107847.

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Sham, P. C. "Statistical methods in psychiatric genetics." Statistical Methods in Medical Research 7, no. 3 (March 1, 1998): 279–300. http://dx.doi.org/10.1191/096228098677382724.

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Sham, Pak C. "Statistical methods in psychiatric genetics." Statistical Methods in Medical Research 7, no. 3 (June 1998): 279–300. http://dx.doi.org/10.1177/096228029800700305.

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GUILLOT, GILLES, RAPHA��L LEBLOIS, AUR��LIE COULON, and ALAIN C. FRANTZ. "Statistical methods in spatial genetics." Molecular Ecology 18, no. 23 (December 2009): 4734–56. http://dx.doi.org/10.1111/j.1365-294x.2009.04410.x.

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Schaid, D. J., X. Tong, B. Larrabee, R. B. Kennedy, G. A. Poland, and J. P. Sinnwell. "Statistical Methods for Testing Genetic Pleiotropy." Genetics 204, no. 2 (August 15, 2016): 483–97. http://dx.doi.org/10.1534/genetics.116.189308.

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Zeegers, M. P. "Statistical Methods in Genetic Epidemiology." Journal of Medical Genetics 41, no. 12 (December 1, 2004): 958. http://dx.doi.org/10.1136/jmg.2004.021113.

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Wyszynski, Diego F. "Statistical Methods in Genetic Epidemiology." American Journal of Human Genetics 76, no. 1 (January 2005): 190. http://dx.doi.org/10.1086/427114.

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Ott, Jurg, and Helen Donis-Keller. "Statistical Methods in Genetic Mapping." Genomics 22, no. 2 (July 1994): 496–97. http://dx.doi.org/10.1006/geno.1994.1421.

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Dissertations / Theses on the topic "Genetics – Statistical methods"

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ZHANG, GE. "STATISTICAL METHODS IN GENETIC ASSOCIATION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744.

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Lange, Christoph. "Generalized estimating equation methods in statistical genetics." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269921.

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Jung, Min Kyung. "Statistical methods for biological applications." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278454.

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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2007.
Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6740. Adviser: Elizabeth A. Housworth. Title from dissertation home page (viewed May 20, 2008).
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Yung, Godwin Yuen Han. "Statistical methods for analyzing genetic sequencing association studies." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493313.

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Case-control genetic sequencing studies are increasingly being conducted to identify rare variants associated with complex diseases. Oftentimes, these studies collect a variety of secondary traits--quantitative and qualitative traits besides the case-control disease status. Reusing the data and studying the association between rare variants and secondary phenotypes provide an attractive and cost effective approach that can lead to discovery of new genetic associations. In Chapter 1, we carry out an extensive investigation of the validity of ad hoc methods, which are simple, computationally efficient methods frequently applied in practice to study the association between secondary phenotypes and single common genetic variants. Though other researchers have investigated the same problem, we make two key contributions to existing literature. First, we show that in taking an ad hoc approach, it may be desirable to adjust for covariates that affect the primary disease in the secondary phenotype model, even though these covariates are not necessarily associated with the secondary phenotype in the population. Second, we show that when the disease is rare, ad hoc methods can lead to severely biased estimation and inference if the true disease model follows a non-logistic model such as the probit model. Spurious associations can be avoided by including interaction terms in the fitted regression model. Our results are justified theoretically and via simulations, and illustrated by a genome-wide association study of smoking using a lung cancer case-control study. In Chapter 2, we consider the problem of testing associations between secondary phenotypes and sets of rare genetic variants. We show that popular region-based methods such as the burden test and the sequence kernel association test (SKAT) can only be applied under the same conditions as those applicable to ad hoc methods (Chapter 1). For a more robust alternative, we propose an inverse-probability-weighted version of the optimal SKAT (SKAT-O) to account for unequal sampling of cases and controls. As an extension of SKAT-O, our approach is data adaptive and includes the weighted burden test and weighted SKAT as special cases. In addition to weighting individuals to account for the biased sampling, we can also consider weighting the variants in SKAT-O. Decreasing the weight of non-causal variants and increasing the weight of causal variants can improve power. However, since researchers do not know which variants are actually causal, it is common practice to weight genetic variants as a function of their minor allele frequencies. This is motivated by the belief that rarer variants are more likely to have larger effects. In Chapter 3, we propose a new unsupervised statistical framework for predicting the functional status of genetic variants. Compared to existing methods, the proposed algorithm integrates a diverse set of annotations---which are partitioned beforehand into multiple groups by the user---and predicts the functional status for each group, taking into account within- and between-group correlations. We demonstrate the advantages of the algorithm through application to real annotation data and conclude with future directions.
Biostatistics
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Shringarpure, Suyash. "Statistical Methods for studying Genetic Variation in Populations." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/117.

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The study of genetic variation in populations is of great interest for the study of the evolutionary history of humans and other species. Improvement in sequencing technology has resulted in the availability of many large datasets of genetic data. Computational methods have therefore become quite important in analyzing these data. Two important problems that have been studied using genetic data are population stratification (modeling individual ancestry with respect to ancestral populations) and genetic association (finding genetic polymorphisms that affect a trait). In this thesis, we develop methods to improve our understanding of these two problems. For the population stratification problem, we develop hierarchical Bayesian models that incorporate the evolutionary processes that are known to affect genetic variation. By developing mStruct, we show that modeling more evolutionary processes improves the accuracy of the recovered population structure. We demonstrate how nonparametric Bayesian processes can be used to address the question of choosing the optimal number of ancestral populations that describe the genetic diversity of a given sample of individuals. We also examine how sampling bias in genotyping study design can affect results of population structure analysis and propose a probabilistic framework for modeling and correcting sample selection bias. Genome-wide association studies (GWAS) have vastly improved our understanding of many diseases. However, such studies have failed to uncover much of the variation responsible for a number of common multi-factorial diseases and complex traits. We show how artificial selection experiments on model organisms can be used to better understand the nature of genetic associations. We demonstrate using simulations that using data from artificial selection experiments improves the performance of conventional methods of performing association. We also validate our approach using semi-simulated data from an artificial selection experiment on Drosophila Melanogaster.
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Yu, Xiaoqing. "Statistical Methods and Analyses for Next-generation Sequencing Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1403708200.

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Cordell, Heather Jane. "Statistical methods in the genetic analysis of type 1 diabetes." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296834.

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Lee, Yiu-fai, and 李耀暉. "Analysis for segmental sharing and linkage disequilibrium: a genomewide association study on myopia." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43912217.

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Allchin, Lorraine Doreen May. "Statistical methods for mapping complex traits." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:65f392ba-1b64-4b00-8871-7cee98809ce1.

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The first section of this thesis addresses the problem of simultaneously identifying multiple loci that are associated with a trait, using a Bayesian Markov Chain Monte Carlo method. It is applicable to both case/control and quantitative data. I present simulations comparing the methods to standard frequentist methods in human case/control and mouse QTL datasets, and show that in the case/control simulations the standard frequentist method out performs my model for all but the highest effect simulations and that for the mouse QTL simulations my method performs as well as the frequentist method in some cases and worse in others. I also present analysis of real data and simulations applying my method to a simulated epistasis data set. The next section was inspired by the challenges involved in applying a Markov Chain Monte Carlo method to genetic data. It is an investigation into the performance and benefits of the Matlab parallel computing toolbox, specifically its implementation of the Cuda programing language to Matlab's higher level language. Cuda is a language which allows computational calculations to be carried out on the computer's graphics processing unit (GPU) rather than its central processing unit (CPU). The appeal of this tool box is its ease of use as few code adaptions are needed. The final project of this thesis was to develop an HMM for reconstructing the founders of sparsely sequenced inbred populations. The motivation here, that whilst sequencing costs are rapidly decreasing, it is still prohibitively expensive to fully sequence a large number of individuals. It was proposed that, for populations descended from a known number of founders, it would be possible to sequence these individuals with a very low coverage, use a hidden Markov model (HMM) to represent the chromosomes as mosaics of the founders, then use these states to impute the missing data. For this I developed a Viterbi algorithm with a transition probability matrix based on recombination rate which changes for each observed state.
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Vaez, Torshizi Rasoul. "Quantitative genetic analyses of production and reproduction traits in Australian merino sheep." Thesis, The University of Sydney, 1996. https://hdl.handle.net/2123/27593.

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Restricted Maximum Likelihood (REML) procedures based on a derivative-free algorithm using the Simplex method and fitting an animal model were used to estimate variance and covariance components for several performances of productive traits, namely, body weight measured at birth, weaning, 10 month, 16 month and 22 months of age, greasy fleece average daily gain to 4, 10, 16 and 22 months of age, clean fleece average daily gain to 10, 16, 22 months of age and mean fibre diameter measured at 10, 16 and 22 months of age. For these traits, the importance of maternal effects, either additive genetic or environmental, were investigated. The interrelationship among the performances of each trait were studied, and then were used to determine the efficiencies of indirect selection at early ages compared with later ages for improvement of an animal's lifetime production.
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Books on the topic "Genetics – Statistical methods"

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Prem, Narain. Statistical genetics. New York: Wiley, 1990.

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Mukhopadhyay, Indranil, and Partha Pratim Majumder. Statistical Methods in Human Genetics. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3220-7.

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J, Balding D., Bishop M. J, and Cannings C. 1942-, eds. Handbook of statistical genetics. 3rd ed. Chichester, England: John Wiley & Sons, 2007.

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M, Neale Benjamin, ed. Statistical genetics: Gene mapping through linkage and association. New York: Taylor & Francis Group, 2008.

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1932-, Elston Robert C., Olson Jane M, and Palmer Lyle, eds. Biostatistical genetics and genetic epidemiology. Chichester, England: New York, NY, USA, 2002.

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Sham, Pak. Statistics in human genetics. London: Arnold, 1998.

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Sham, P. Statistics in human genetics. London: Arnold, 1997.

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1947-, Gianola Daniel, ed. Likelihood, Bayesian and MCMC methods in quantitative genetics. New York: Springer-Verlag, 2002.

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Neale, Benjamin M. Statistical genetics: Gene mapping through linkage and association. New York: Taylor & Francis Group, 2008.

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J, Camp Nicola, and Cox Angela 1961-, eds. Quantitative trait loci: Methods and protocols. Totowa, N.J: Humana Press, 2002.

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Book chapters on the topic "Genetics – Statistical methods"

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Sorensen, Daniel. "Bayesian Methods." In Statistical Learning in Genetics, 141–206. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35851-7_4.

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Sorensen, Daniel. "Shrinkage Methods." In Statistical Learning in Genetics, 299–332. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35851-7_7.

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Ewens, W. J. "Statistical Methods in Human Genetics." In Statistics in Genetics, 147–64. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3103-3_8.

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Bulinski, Alexander. "Some Statistical Methods in Genetics." In Stochastic Geometry, Spatial Statistics and Random Fields, 293–320. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10064-7_10.

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Lewin, A., and S. Richardson. "Bayesian Methods for Microarray Data." In Handbook of Statistical Genetics, 267–95. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch8.

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Sorensen, Daniel. "Nonparametric Methods: A Selected Overview." In Statistical Learning in Genetics, 445–540. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35851-7_11.

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Penny, D., M. D. Hendy, and B. R. Holland. "Phylogenetics: Parsimony, Networks, and Distance Methods." In Handbook of Statistical Genetics, 489–532. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch16.

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Liu, Jun S., and T. Logvinenko. "Bayesian Methods in Biological Sequence Analysis." In Handbook of Statistical Genetics, 67–96. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch3.

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Foulkes, Andrea S. "Methods for Unobservable Phase." In Applied Statistical Genetics with R, 129–56. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-89554-3_5.

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Zeng, Zhao-Bang. "Quantitative Trait Loci: Statistical Methods for Mapping Their Positions." In Encyclopedia of Genetics, 904–9. New York: Routledge, 2014. http://dx.doi.org/10.4324/9781315073972-136.

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Conference papers on the topic "Genetics – Statistical methods"

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Fan, QiaoChu, Zi jie Lu, and Yu chen Liu. "Statistical research methods for genetics." In 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), edited by Chi-Hua Chen, Xuexia Ye, and Hari Mohan Srivastava. SPIE, 2022. http://dx.doi.org/10.1117/12.2639273.

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Lutze, Margaret, Nancy J. Cox, Vivianne C. Smith, and Joel Pokorny. "Genetics of Rayleigh matches and photometric matches in normals." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.mz3.

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At the 1988 ARVO meeting we presented Rayleigh match midpoint and photometric match data collected from a population of nonrelated males and from family members. Photometric matches between a 667- and 551-nm stimulus were obtained using heterochromatic modulation photometry (HMP). We employed statistical methods to determine whether the source of variation for each of these traits was due to allelic variation at a single gene locus, variation at multiple loci (polygenic), or environmental factors. Results indicated that variations in Rayleigh match midpoints and HMP photometric matches in observers with normal color vision were each consistent with allelic variation at a single gene locus.
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Ueno, Augusto Yoshiro, Ivo Emilio da Cruz Jung, Ivana Beatrice Mânica da Cruz, and Fernanda Barbisan. "Depression and psychological distress in elders are influenced by the antioxidant enzyme SOD2." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.003.

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Introduction: Depression and psychological stress have high prevalence and incidence rates, affecting the individual welfare and increasing the risks for non-infectious chronic diseases. Studies have shown relations between inflammation and oxidative stress. In genetics, the single nucleotide polymorphism (SNP), inside the superoxide dismutase gene (Val16Ala-SOD2), is an important study subject to comprehend the risks of developing depression because its different genotypes can impact the balance between superoxide and hydrogen peroxide. The genotype VV favors the superoxide, the AA favors the peroxide and the AV generates similar amounts. Objectives: Evaluate the relation between oxidative unbalance, generated by Val16Ala-SOD2 SNP, and the rates of depression in elders. Methods: The study, approved by the ethics committee of UFSM, was a case-control analysis to examine the association between Val16Ala-SOD2 SNP, depression and stress in elders. Genetical analysis was made by polymerase chain reactions. The sample had 612 elders from Gravataí (RS). Depression was diagnosed using the geriatric depression scale- 15 and the stress by self perception. Statistical analysis was made by SSPS. Results: From the 612 elders (with similar ages and lifestyles), 115 were diagnosed with depression; the other 497 composed the control group. The analyses showed significantly higher frequency of the genotype VV in those who had depression, compared with the allele A. Conclusion: The results indicate strong association of the Val16Ala-SOD2 SNP, the risks of depression and psychological stress, probably due to the increasing oxidative stress and inflammatory state associated with the recessive genotype, VV.
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Reeves, C. R. "Genetic algorithms and statistical methods: a comparison." In 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA). IEE, 1995. http://dx.doi.org/10.1049/cp:19951038.

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Ismail, Zuhaimy, Mohd Zulariffin Md Maarof, and Mohammad Fadzli. "Alteration of Box-Jenkins methodology by implementing genetic algorithm method." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907522.

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Duca, Maria, Angela Port, and Rodica Martea. "Multivariate statistical methods in analysis of broomrape genetic diversity." In XIth International Congress of Geneticists and Breeders from the Republic of Moldova. Scientific Association of Geneticists and Breeders of the Republic of Moldova, Institute of Genetics, Physiology and Plant Protection, Moldova State University, 2021. http://dx.doi.org/10.53040/cga11.2021.008.

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Gomes, Dora Prata, Inês J. Sequeira, Carlos Figueiredo, José Rueff, and Aldina Brás. "The human chromosomal fragile sites more often involved in constitutional deletions and duplications – A genetic and statistical assessment." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2016 (ICCMSE 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4968684.

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Carata, Serban-Vasile, Veta Ghenescu, Marian Ghenescu, Mihai Chindea, and Roxana Mihaescu. "Salt and Pepper Noise Removal by Combining Genetic Algorithms - Neural Networks and Statistical Methods." In 2018 12th International Conference on Communications (COMM). IEEE, 2018. http://dx.doi.org/10.1109/iccomm.2018.8430175.

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Carata, Serban-Vasile, Veta Ghenescu, Marian Ghenescu, Mihai Chindea, and Roxana Mihaescu. "Salt and Pepper Noise Removal by Combining Genetic Algorithms - Neural Networks and Statistical Methods." In 2018 12th International Conference on Communications (COMM). IEEE, 2018. http://dx.doi.org/10.1109/iccomm.2018.8484807.

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Cruse, Thomas A. "Advances in Probabilistic Methods." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2668.

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Abstract Advances in probabilistic methods to support the design of mechanical systems is now moving to the engineering desktop. Basic modeling strategies are understood and in use in many applications. There are still issues to be resolved in the application of these methods to reliability critical applications. Some of these issues will be highlighted. At the same time, the design of complex aerospace systems requires more than just probabilistic methods as we know them now. The presentation will review a recent study by its author on the development and application of non-traditional and non-deterministic methods new methods to the life cycle design and analysis of extremely complex aerospace systems. Not only “traditional” mathematical analysis methodologies (such as mathematical optimization, Bayesian methods, or robust statistical design) are reviewed, but also “soft” or “non-traditional” methods (such as neural networks, fuzzy logic or genetic algorithms). In particular, emphasis will be given to the synthesis of many of these elements into the design systems of the future.
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Reports on the topic "Genetics – Statistical methods"

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Ott, Jurg. Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1996. http://dx.doi.org/10.21236/ada326461.

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Ott, Jurg. Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada337861.

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Ott, Jurg. Statistical Genetic Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada301699.

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Hovav, Ran, Peggy Ozias-Akins, and Scott A. Jackson. The genetics of pod-filling in peanut under water-limiting conditions. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597923.bard.

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Pod-filling, an important yield-determining stage is strongly influenced by water stress. This is particularly true for peanut (Arachishypogaea), wherein pods are developed underground and are directly affected by the water condition. Pod-filling in peanut has a significant genetic component as well, since genotypes are considerably varied in their pod-fill (PF) and seed-fill (SF) potential. The goals of this research were to: Examine the effects of genotype, irrigation, and genotype X irrigation on PF and SF. Detect global changes in mRNA and metabolites levels that accompany PF and SF. Explore the response of the duplicate peanut pod transcriptome to drought stress. Study how entire duplicated PF regulatory processes are networked within a polyploid organism. Discover locus-specific SNP markers and map pod quality traits under different environments. The research included genotypes and segregating populations from Israel and US that are varied in PF, SF and their tolerance to water deficit. Initially, an extensive field trial was conducted to investigate the effects of genotype, irrigation, and genotype X irrigation on PF and SF. Significant irrigation and genotypic effect was observed for the two main PF related traits, "seed ratio" and "dead-end ratio", demonstrating that reduction in irrigation directly influences the developing pods as a result of low water potential. Although the Irrigation × Genotype interaction was not statistically significant, one genotype (line 53) was found to be more sensitive to low irrigation treatments. Two RNAseq studies were simultaneously conducted in IL and the USA to characterize expression changes that accompany shell ("source") and seed ("sink") biogenesis in peanut. Both studies showed that SF and PF processes are very dynamic and undergo very rapid change in the accumulation of RNA, nutrients, and oil. Some genotypes differ in transcript accumulation rates, which can explain their difference in SF and PF potential; like cvHanoch that was found to be more enriched than line 53 in processes involving the generation of metabolites and energy at the beginning of seed development. Interestingly, an opposite situation was found in pericarp development, wherein rapid cell wall maturation processes were up-regulated in line 53. Although no significant effect was found for the irrigation level on seed transcriptome in general, and particularly on subgenomic assignment (that was found almost comparable to a 1:1 for A- and B- subgenomes), more specific homoeologous expression changes associated with particular biosynthesis pathways were found. For example, some significant A- and B- biases were observed in particular parts of the oil related gene expression network and several candidate genes with potential influence on oil content and SF were further examined. Substation achievement of the current program was the development and application of new SNP detection and mapping methods for peanut. Two major efforts on this direction were performed. In IL, a GBS approach was developed to map pod quality traits on Hanoch X 53 F2/F3 generations. Although the GBS approach was found to be less effective for our genetic system, it still succeeded to find significant mapping locations for several traits like testa color (linkage A10), number of seeds/pods (A5) and pod wart resistance (B7). In the USA, a SNP array was developed and applied for peanut, which is based on whole genome re-sequencing of 20 genotypes. This chip was used to map pod quality related traits in a Tifrunner x NC3033 RIL population. It was phenotyped for three years, including a new x-ray method to phenotype seed-fill and seed density. The total map size was 1229.7 cM with 1320 markers assigned. Based on this linkage map, 21 QTLs were identified for the traits 16/64 weight, kernel percentage, seed and pod weight, double pod and pod area. Collectively, this research serves as the first fundamental effort in peanut for understanding the PF and SF components, as a whole, and as influenced by the irrigation level. Results of the proposed study will also generate information and materials that will benefit peanut breeding by facilitating selection for reduced linkage drag during introgression of disease resistance traits into elite cultivars. BARD Report - Project4540 Page 2 of 10
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Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600017.bard.

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The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) determine causative polymorphisms based on concordance between the bulls’ genotypes for specific polymorphisms and their status for a QTL; (4) validate putative quantitative trait variants by genotyping a sample of Israeli Holstein cows; and (5) perform gene expression analysis using statistical methodologies, including determination of signatures of selection, based on somatic cells of cows that are homozygous for contrasting quantitative trait variants; and (6) analyze genes with putative quantitative trait variants using data mining techniques. Current methods for genomic evaluation are based on population-wide linkage disequilibrium between markers and actual alleles that affect traits of interest. Those methods have approximately doubled the rate of genetic gain for most traits in the U.S. Holstein population. With determination of causative polymorphisms, increasing the accuracy of genomic evaluations should be possible by including those genotypes as fixed effects in the analysis models. Determination of causative polymorphisms should also yield useful information on gene function and genetic architecture of complex traits. Concordance between QTL genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects that are segregating in the U.S. Holstein population; a probability of <10²⁰ was used to accept the null hypothesis that no segregating gene within the chromosomal segment was affecting the trait. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome. Variant sites were identified from previous studies (such as the 1000 Bull Genomes Project) and from DNA sequencing of bulls unique to this project, which is one of the largest marker variant surveys conducted for the Holstein breed of cattle. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: (1) complete or nearly complete concordance, (2) nominal significance of the polymorphism effect after correction for all other polymorphisms, and (3) marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. The missense polymorphism Phe279Tyr in GHR at 31,909,478 base pairs on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage, 12 additional missensepolymorphisms on chromosome 14 were found that had nearly complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The markers used in routine U.S. genomic evaluations were increased from 60,000 to 80,000 by adding markers for known QTLs and markers detected in BARD and other research projects. Objectives 1 and 2 were completely accomplished, and objective 3 was partially accomplished. Because no new clear-cut causative polymorphisms were discovered, objectives 4 through 6 were not completed.
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6

Hutchinson, M. L., J. E. L. Corry, and R. H. Madden. A review of the impact of food processing on antimicrobial-resistant bacteria in secondary processed meats and meat products. Food Standards Agency, October 2020. http://dx.doi.org/10.46756/sci.fsa.bxn990.

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For meat and meat products, secondary processes are those that relate to the downstream of the primary chilling of carcasses. Secondary processes include maturation chilling, deboning, portioning, mincing and other operations such as thermal processing (cooking) that create fresh meat, meat preparations and ready-to-eat meat products. This review systematically identified and summarised information relating to antimicrobial resistance (AMR) during the manufacture of secondary processed meatand meat products (SPMMP). Systematic searching of eight literature databases was undertaken and the resultantpapers were appraised for relevance to AMR and SPMMP. Consideration was made that the appraisal scores, undertaken by different reviewers, were consistent. Appraisal reduced the 11,000 initially identified documents to 74, which indicated that literature relating to AMR and SPMMP was not plentiful. A wide range of laboratory methods and breakpoint values (i.e. the concentration of antimicrobial used to assess sensitivity, tolerance or resistance) were used for the isolation of AMR bacteria.The identified papers provided evidence that AMR bacteria could be routinely isolated from SPMMP. There was no evidence that either confirmed or refuted that genetic materials capable of increasing AMR in non-AMR bacteria were present unprotected (i.e. outside of a cell or a capsid) in SPMMP. Statistical analyses were not straightforward because different authors used different laboratory methodologies.However, analyses using antibiotic organised into broadly-related groups indicated that Enterobacteriaceaeresistant to third generation cephalosporins might be an area of upcoming concern in SPMMP. The effective treatment of patients infected with Enterobacteriaceaeresistant to cephalosporins are a known clinical issue. No AMR associations with geography were observed and most of the publications identified tended to be from Europe and the far east.AMR Listeria monocytogenes and lactic acid bacteria could be tolerant to cleaning and disinfection in secondary processing environments. The basis of the tolerance could be genetic (e.g. efflux pumps) or environmental (e.g. biofilm growth). Persistent, plant resident, AMR L. monocytogenes were shown by one study to be the source of final product contamination. 4 AMR genes can be present in bacterial cultures used for the manufacture of fermented SPMMP. Furthermore, there was broad evidence that AMR loci could be transferred during meat fermentation, with refrigeration temperatures curtailing transfer rates. Given the potential for AMR transfer, it may be prudent to advise food business operators (FBOs) to use fermentation starter cultures that are AMR-free or not contained within easily mobilisable genetic elements. Thermal processing was seen to be the only secondary processing stage that served as a critical control point for numbers of AMR bacteria. There were significant linkages between some AMR genes in Salmonella. Quaternary ammonium compound (QAC) resistance genes were associated with copper, tetracycline and sulphonamide resistance by virtue of co-location on the same plasmid. No evidence was found that either supported or refuted that there was any association between AMR genes and genes that encoded an altered stress response or enhanced the survival of AMR bacteria exposed to harmful environmental conditions.
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7

Jorgensen, Frieda, Andre Charlett, Craig Swift, Anais Painset, and Nicolae Corcionivoschi. A survey of the levels of Campylobacter spp. contamination and prevalence of selected antimicrobial resistance determinants in fresh whole UK-produced chilled chickens at retail sale (non-major retailers). Food Standards Agency, June 2021. http://dx.doi.org/10.46756/sci.fsa.xls618.

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Campylobacter spp. are the most common bacterial cause of foodborne illness in the UK, with chicken considered to be the most important vehicle for this organism. The UK Food Standards Agency (FSA) agreed with industry to reduce Campylobacter spp. contamination in raw chicken and issued a target to reduce the prevalence of the most contaminated chickens (those with more than 1000 cfu per g chicken neck skin) to below 10 % at the end of the slaughter process, initially by 2016. To help monitor progress, a series of UK-wide surveys were undertaken to determine the levels of Campylobacter spp. on whole UK-produced, fresh chicken at retail sale in the UK. The data obtained for the first four years was reported in FSA projects FS241044 (2014/15) and FS102121 (2015 to 2018). The FSA has indicated that the retail proxy target for the percentage of highly contaminated raw whole retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target. This report presents results from testing chickens from non-major retailer stores (only) in a fifth survey year from 2018 to 2019. In line with previous practise, samples were collected from stores distributed throughout the UK (in proportion to the population size of each country). Testing was performed by two laboratories - a Public Health England (PHE) laboratory or the Agri-Food & Biosciences Institute (AFBI), Belfast. Enumeration of Campylobacter spp. was performed using the ISO 10272-2 standard enumeration method applied with a detection limit of 10 colony forming units (cfu) per gram (g) of neck skin. Antimicrobial resistance (AMR) to selected antimicrobials in accordance with those advised in the EU harmonised monitoring protocol was predicted from genome sequence data in Campylobacter jejuni and Campylobacter coli isolates The percentage (10.8%) of fresh, whole chicken at retail sale in stores of smaller chains (for example, Iceland, McColl’s, Budgens, Nisa, Costcutter, One Stop), independents and butchers (collectively referred to as non-major retailer stores in this report) in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. has decreased since the previous survey year but is still higher than that found in samples from major retailers. 8 whole fresh raw chickens from non-major retailer stores were collected from August 2018 to July 2019 (n = 1009). Campylobacter spp. were detected in 55.8% of the chicken skin samples obtained from non-major retailer shops, and 10.8% of the samples had counts above 1000 cfu per g chicken skin. Comparison among production plant approval codes showed significant differences of the percentages of chicken samples with more than 1000 cfu per g, ranging from 0% to 28.1%. The percentage of samples with more than 1000 cfu of Campylobacter spp. per g was significantly higher in the period May, June and July than in the period November to April. The percentage of highly contaminated samples was significantly higher for samples taken from larger compared to smaller chickens. There was no statistical difference in the percentage of highly contaminated samples between those obtained from chicken reared with access to range (for example, free-range and organic birds) and those reared under standard regime (for example, no access to range) but the small sample size for organic and to a lesser extent free-range chickens, may have limited the ability to detect important differences should they exist. Campylobacter species was determined for isolates from 93.4% of the positive samples. C. jejuni was isolated from the majority (72.6%) of samples while C. coli was identified in 22.1% of samples. A combination of both species was found in 5.3% of samples. C. coli was more frequently isolated from samples obtained from chicken reared with access to range in comparison to those reared as standard birds. C. jejuni was less prevalent during the summer months of June, July and August compared to the remaining months of the year. Resistance to ciprofloxacin (fluoroquinolone), erythromycin (macrolide), tetracycline, (tetracyclines), gentamicin and streptomycin (aminoglycosides) was predicted from WGS data by the detection of known antimicrobial resistance determinants. Resistance to ciprofloxacin was detected in 185 (51.7%) isolates of C. jejuni and 49 (42.1%) isolates of C. coli; while 220 (61.1%) isolates of C. jejuni and 73 (62.9%) isolates of C. coli isolates were resistant to tetracycline. Three C. coli (2.6%) but none of the C. jejuni isolates harboured 23S mutations predicting reduced susceptibility to erythromycin. Multidrug resistance (MDR), defined as harbouring genetic determinants for resistance to at least three unrelated antimicrobial classes, was found in 10 (8.6%) C. coli isolates but not in any C. jejuni isolates. Co-resistance to ciprofloxacin and erythromycin was predicted in 1.7% of C. coli isolates. 9 Overall, the percentages of isolates with genetic AMR determinants found in this study were similar to those reported in the previous survey year (August 2016 to July 2017) where testing was based on phenotypic break-point testing. Multi-drug resistance was similar to that found in the previous survey years. It is recommended that trends in AMR in Campylobacter spp. isolates from retail chickens continue to be monitored to realise any increasing resistance of concern, particulary to erythromycin (macrolide). Considering that the percentage of fresh, whole chicken from non-major retailer stores in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. continues to be above that in samples from major retailers more action including consideration of interventions such as improved biosecurity and slaughterhouse measures is needed to achieve better control of Campylobacter spp. for this section of the industry. The FSA has indicated that the retail proxy target for the percentage of highly contaminated retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target.
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8

Tipton, Kelley, Brian F. Leas, Emilia Flores, Christopher Jepson, Jaya Aysola, Jordana Cohen, Michael Harhay, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), December 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-searched reference lists of relevant studies and reviewed suggestions from experts and stakeholders. Review methods. Searches identified 11,500 unique records. Using predefined criteria and dual review, we screened and selected studies to assess one or both Key Questions (KQs): (1) the effect of algorithms on racial and ethnic disparities in health and healthcare outcomes and (2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Outcomes of interest included access to healthcare, quality of care, and health outcomes. We assessed studies’ methodologic risk of bias (ROB) using the ROBINS-I tool and piloted an appraisal supplement to assess racial and ethnic equity-related ROB. We completed a narrative synthesis and cataloged study characteristics and outcome data. We also examined four Contextual Questions (CQs) designed to explore the context and capture insights on practical aspects of potential algorithmic bias. CQ 1 examines the problem’s scope within healthcare. CQ 2 describes recently emerging standards and guidance on how racial and ethnic bias can be prevented or mitigated during algorithm development and deployment. CQ 3 explores stakeholder awareness and perspectives about the interaction of algorithms and racial and ethnic disparities in health and healthcare. We addressed these CQs through supplemental literature reviews and conversations with experts and key stakeholders. For CQ 4, we conducted an in-depth analysis of a sample of six algorithms that have not been widely evaluated before in the published literature to better understand how their design and implementation might contribute to disparities. Results. Fifty-eight studies met inclusion criteria, of which three were included for both KQs. One study was a randomized controlled trial, and all others used cohort, pre-post, or modeling approaches. The studies included numerous types of clinical assessments: need for intensive care or high-risk care management; measurement of kidney or lung function; suitability for kidney or lung transplant; risk of cardiovascular disease, stroke, lung cancer, prostate cancer, postpartum depression, or opioid misuse; and warfarin dosing. We found evidence suggesting that algorithms may: (a) reduce disparities (i.e., revised Kidney Allocation System, prostate cancer screening tools); (b) perpetuate or exacerbate disparities (e.g., estimated glomerular filtration rate [eGFR] for kidney function measurement, cardiovascular disease risk assessments); and/or (c) have no effect on racial or ethnic disparities. Algorithms for which mitigation strategies were identified are included in KQ 2. We identified six types of strategies often used to mitigate the potential of algorithms to contribute to disparities: removing an input variable; replacing a variable; adding one or more variables; changing or diversifying the racial and ethnic composition of the patient population used to train or validate a model; creating separate algorithms or thresholds for different populations; and modifying the statistical or analytic techniques used by an algorithm. Most mitigation efforts improved proximal outcomes (e.g., algorithmic calibration) for targeted populations, but it is more challenging to infer or extrapolate effects on longer term outcomes, such as racial and ethnic disparities. The scope of racial and ethnic bias related to algorithms and their application is difficult to quantify, but it clearly extends across the spectrum of medicine. Regulatory, professional, and corporate stakeholders are undertaking numerous efforts to develop standards for algorithms, often emphasizing the need for transparency, accountability, and representativeness. Conclusions. Algorithms have been shown to potentially perpetuate, exacerbate, and sometimes reduce racial and ethnic disparities. Disparities were reduced when race and ethnicity were incorporated into an algorithm to intentionally tackle known racial and ethnic disparities in resource allocation (e.g., kidney transplant allocation) or disparities in care (e.g., prostate cancer screening that historically led to Black men receiving more low-yield biopsies). It is important to note that in such cases the rationale for using race and ethnicity was clearly delineated and did not conflate race and ethnicity with ancestry and/or genetic predisposition. However, when algorithms include race and ethnicity without clear rationale, they may perpetuate the incorrect notion that race is a biologic construct and contribute to disparities. Finally, some algorithms may reduce or perpetuate disparities without containing race and ethnicity as an input. Several modeling studies showed that applying algorithms out of context of original development (e.g., illness severity scores used for crisis standards of care) could perpetuate or exacerbate disparities. On the other hand, algorithms may also reduce disparities by standardizing care and reducing opportunities for implicit bias (e.g., Lung Allocation Score for lung transplantation). Several mitigation strategies have been shown to potentially reduce the contribution of algorithms to racial and ethnic disparities. Results of mitigation efforts are highly context specific, relating to unique combinations of algorithm, clinical condition, population, setting, and outcomes. Important future steps include increasing transparency in algorithm development and implementation, increasing diversity of research and leadership teams, engaging diverse patient and community groups in the development to implementation lifecycle, promoting stakeholder awareness (including patients) of potential algorithmic risk, and investing in further research to assess the real-world effect of algorithms on racial and ethnic disparities before widespread implementation.
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