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Journal articles on the topic 'Intelligence – genetics'

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Published: 25 July 2024

Last updated: 26 July 2024

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1

Farzan, Raed. "Artificial intelligence in Immuno-genetics." Bioinformation 20, no. 1 (January 31, 2024): 29–35. http://dx.doi.org/10.6026/973206300200029.

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Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.

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2

BARNETT, S. A. "GENETICS OF INTELLIGENCE." Developmental Medicine & Child Neurology 5, no. 5 (November 12, 2008): 522. http://dx.doi.org/10.1111/j.1469-8749.1963.tb10712.x.

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3

Deary, Ian J., Frank M. Spinath, and Timothy C. Bates. "Genetics of intelligence." European Journal of Human Genetics 14, no. 6 (May 24, 2006): 690–700. http://dx.doi.org/10.1038/sj.ejhg.5201588.

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4

Plomin, Robert, and Jenae Neiderhiser. "Quantitative Genetics, Molecular Genetics, and Intelligence." Intelligence 15, no. 4 (October 1991): 369–87. http://dx.doi.org/10.1016/0160-2896(91)90001-t.

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5

Sternberg, Robert J., Elena L. Grigorenko, and Kenneth K. Kidd. "Intelligence, race, and genetics." American Psychologist 60, no. 1 (2005): 46–59. http://dx.doi.org/10.1037/0003-066x.60.1.46.

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6

VASYLKIVSKYI, Mikola, Ganna VARGATYUK, and Olga BOLDYREVA. "INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (February 23, 2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.

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The peculiarities of the implementation of the 6G intelligent radio interface infrastructure, which will use an individual configuration for each individual subscriber application and flexible services with lower overhead costs, have been studied. A personalized infrastructure consisting of an AI-enabled intelligent physical layer, an intelligent MAC controller, and an intelligent protocol is considered, followed by a potentially novel AI-based end-to-end (E2E) device. The intelligent controller is investigated, in particular the intelligent functions at the MAC level, which may become key components of the intelligent controller in the future. The joint optimization of these components, which will provide better system performance, is considered. It was determined that instead of using a complex mathematical method of optimization, it is possible to use machine learning, which has less complexity and can adapt to network conditions. A 6G radio interface design based on a combination of model-driven and data-driven artificial intelligence is investigated and is expected to provide customized radio interface optimization from pre-configuration to self-learning. The specifics of configuring the network scheme and transmission parameters at the level of subscriber equipment and services using a personalized radio interface to maximize the individual user experience without compromising the throughput of the system as a whole are determined. Artificial intelligence is considered, which will be a built-in function of the radio interface that creates an intelligent physical layer and is responsible for MAC access control, network management optimization (such as load balancing and power saving), replacing some non-linear or non-convex algorithms in receiver modules or compensation of shortcomings in non-linear models. Built-in intelligence has been studied, which will make the 6G physical layer more advanced and efficient, facilitate the optimization of structural elements of the physical layer and procedural design, including the possible change of the receiver architecture, will help implement new detection and positioning capabilities, which, in turn, will significantly affect the design of radio interface components. The requirements for the 6G network are defined, which provide for the creation of a single network with scanning and communication functions, which must be integrated into a single structure at the stage of radio interface design. The specifics of carefully designing a communication and scanning network that will offer full scanning capabilities and more fully meet all key performance indicators in the communications industry are explored.

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Krittanawong, Chayakrit, Kipp W. Johnson, Edward Choi, Scott Kaplin, Eric Venner, Mullai Murugan, Zhen Wang, et al. "Artificial Intelligence and Cardiovascular Genetics." Life 12, no. 2 (February 14, 2022): 279. http://dx.doi.org/10.3390/life12020279.

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Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.

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8

Plomin, Robert, and Frank M. Spinath. "Intelligence: Genetics, Genes, and Genomics." Journal of Personality and Social Psychology 86, no. 1 (January 2004): 112–29. http://dx.doi.org/10.1037/0022-3514.86.1.112.

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9

Plomin, Robert, and Sophie von Stumm. "The new genetics of intelligence." Nature Reviews Genetics 19, no. 3 (January 8, 2018): 148–59. http://dx.doi.org/10.1038/nrg.2017.104.

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10

Plomin, Robert, and Stephen A. Petrill. "Genetics and intelligence: What's new?" Intelligence 24, no. 1 (January 1997): 53–77. http://dx.doi.org/10.1016/s0160-2896(97)90013-1.

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11

Velden, M. "Smarten up on intelligence genetics." Nature 503, no. 7476 (November 2013): 342. http://dx.doi.org/10.1038/503342c.

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12

Johnson, Wendy. "Understanding the Genetics of Intelligence." Current Directions in Psychological Science 19, no. 3 (June 2010): 177–82. http://dx.doi.org/10.1177/0963721410370136.

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13

Lombroso, Paul J., ROBERT J. STERNBERG, and ELENA L. GRIGORENKO. "Genetics of Childhood Disorders: I. Genetics and Intelligence." Journal of the American Academy of Child & Adolescent Psychiatry 38, no. 4 (April 1999): 486–88. http://dx.doi.org/10.1097/00004583-199904000-00024.

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14

KAUFMAN, ALAN S. "Genetics of Childhood Disorders: II. Genetics and Intelligence." Journal of the American Academy of Child & Adolescent Psychiatry 38, no. 5 (May 1999): 626–28. http://dx.doi.org/10.1097/00004583-199905000-00029.

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Lombroso, Paul J., and ROBERT PLOMIN. "Genetics of Childhood Disorders: III. Genetics and Intelligence." Journal of the American Academy of Child & Adolescent Psychiatry 38, no. 6 (June 1999): 786–88. http://dx.doi.org/10.1097/00004583-199906000-00030.

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16

Solomon, Benjamin D. "Can artificial intelligence save medical genetics?" American Journal of Medical Genetics Part A 188, no. 2 (October 11, 2021): 397–99. http://dx.doi.org/10.1002/ajmg.a.62538.

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Toga, Arthur W., and Paul M. Thompson. "GENETICS OF BRAIN STRUCTURE AND INTELLIGENCE." Annual Review of Neuroscience 28, no. 1 (July 21, 2005): 1–23. http://dx.doi.org/10.1146/annurev.neuro.28.061604.135655.

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18

Kristensen, Vessela, and Dag Undlien. "Artificial intelligence (AI) tools in genetics." Open Access Government 42, no. 1 (April 15, 2024): 134–35. http://dx.doi.org/10.56367/oag-042-11133.

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Artificial intelligence (AI) tools in genetics Vessela Kristensen and Dag Undlien uncover AI tools in genetics, from variant recognition to clinical implementation. Most people are curious about how their bodies work (and the ways they occasionally do not). This curiosity extends towards how our bodies are built, their functions, and what maintains life and health. Most people think that science is remote from the lives they lead, and the decisions that they make day by day, but this is far from the truth. Our understanding of genetics may affect our choices at our doctor’s office about our healthcare and reproductive decisions, including family planning.

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19

Antonelli-Ponti, Mayra, and Madeline Crosswaite. "Teachers' Perceptions about the Etiology of Intelligence and Learning Difficulties." International Journal of Educational Psychology 8, no. 2 (June 24, 2019): 162. http://dx.doi.org/10.17583/ijep.2019.3777.

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The etiology of intelligence and learning difficulties are interpreted and perceived in different ways within society. The present study aims to explore the perceptions of a sample of n=501 Brazilian teachers regarding genetic and environmental influences on intelligence and learning difficulties. Using numerical scales, it was observed that importance was ascribed by teachers to genetic and environmental influences across both the intelligence and learning difficulties domains. A multiple choice items test revealed differences on the perceptions of teachers according to gender, age, schooling, area of knowledge, income, years of experience, knowledge of genetics, and having studied genetics. Responses favouring genetic explanations were associated with certain demographic factors while the perception that only environment affects the various domains was not associated with any specific demographics.

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Penke, Lars, Jaap J. A. Denissen, and Geoffrey F. Miller. "The evolutionary genetics of personality." European Journal of Personality 21, no. 5 (August 2007): 549–87. http://dx.doi.org/10.1002/per.629.

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Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics. We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation‐selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation‐selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model. This evolutionary genetic framework highlights the role of gene‐environment interactions in the study of personality, yields new insight into the person‐situation‐debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality. Copyright © 2007 John Wiley & Sons, Ltd.

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21

Greenspan, Neil S. "Genes, Heritability, ‘Race’, and Intelligence: Misapprehensions and Implications." Genes 13, no. 2 (February 15, 2022): 346. http://dx.doi.org/10.3390/genes13020346.

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The role of genetics in determining measured differences in mean IQ between putative racial groups has been a focus of intense discussion and disagreement for more than 50 years. While the last several decades of research have definitively demonstrated that genetic variation can influence measures of cognitive function, the inferences drawn by some participants in the controversy regarding the implications of these findings for racial differences in cognitive ability are highly dubious. Of equal importance, there is no compelling scientific rationale for focusing on and devoting substantial effort to determining mean differences in intelligence or other cognitive functions between groups with incompletely defined and dynamic (and therefore not definitively definable) boundaries.

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22

Piffer, Davide. "Sex Differences in Intelligence: A Genetics Perspective." Mankind Quarterly 58, no. 1 (2017): 112–16. http://dx.doi.org/10.46469/mq.2017.58.1.10.

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23

Arslan, Ruben, and Lars Penke. "Zeroing in on the Genetics of Intelligence." Journal of Intelligence 3, no. 2 (April 20, 2015): 41–45. http://dx.doi.org/10.3390/jintelligence3020041.

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24

Plomin, R., and I. J. Deary. "Genetics and intelligence differences: five special findings." Molecular Psychiatry 20, no. 1 (September 16, 2014): 98–108. http://dx.doi.org/10.1038/mp.2014.105.

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Narita, Akira, Masao Ueki, and Gen Tamiya. "Artificial intelligence powered statistical genetics in biobanks." Journal of Human Genetics 66, no. 1 (August 11, 2020): 61–65. http://dx.doi.org/10.1038/s10038-020-0822-y.

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26

Paul, Diane B. "Textbook Treatments of the Genetics of Intelligence." Quarterly Review of Biology 60, no. 3 (September 1985): 317–26. http://dx.doi.org/10.1086/414428.

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Shakeshaft, Nicholas G., Maciej Trzaskowski, Andrew McMillan, Eva Krapohl, Michael A. Simpson, Avi Reichenberg, Martin Cederlöf, Henrik Larsson, Paul Lichtenstein, and Robert Plomin. "Thinking positively: The genetics of high intelligence." Intelligence 48 (January 2015): 123–32. http://dx.doi.org/10.1016/j.intell.2014.11.005.

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28

Martschenko, Daphne. "DNA Dreams': Teacher Perspectives on the Role and Relevance of Genetics for Education." Research in Education 107, no. 1 (August 21, 2019): 33–54. http://dx.doi.org/10.1177/0034523719869956.

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Behavioural genetics regards intelligence and educational attainment as highly heritable (genetically influenced) and polygenic (influenced by many genes) traits. Researchers in the field have moved beyond identifying whether and how much genes influence a given outcome to trying to pinpoint the genetic markers that help predict them. In more recent years, behavioural genetics research has attempted to cross-over into the field of education, looking to play a role in education research and the construction of education policy. In response to these developments, this paper explores PreK-12 American educators’ perceptions of intelligence in relation to genetics and their views on the relevance of behavioural genetics findings for education. It does so within the context of an ugly history tied to race and racism and an uncertain future. Findings from this mixed-methods study suggest that US teachers believe that genetics play an important role in a student’s intelligence and academic achievement. Furthermore, teachers are open to learning more about the inclusion of genetics research in education policy. At the same time, however, teachers believe that the environment, and in particular parents and a child’s home environment, plays a substantial role in a student’s abilities and education outcomes.

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Elliott, Maxwell L., Daniel W. Belsky, Kevin Anderson, David L. Corcoran, Tian Ge, Annchen Knodt, Joseph A. Prinz, et al. "A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains." Cerebral Cortex 29, no. 8 (September 12, 2018): 3496–504. http://dx.doi.org/10.1093/cercor/bhy219.

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Abstract People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.

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Lett, Tristram A., Bob O. Vogel, Stephan Ripke, Carolin Wackerhagen, Susanne Erk, Swapnil Awasthi, Vassily Trubetskoy, et al. "Cortical Surfaces Mediate the Relationship Between Polygenic Scores for Intelligence and General Intelligence." Cerebral Cortex 30, no. 4 (December 11, 2019): 2708–19. http://dx.doi.org/10.1093/cercor/bhz270.

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Abstract Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surface area (SA) in two large imaging-genetics samples (IMAGEN N = 1651; IntegraMooDS N = 742). PSi explained up to 5.1% of the variance of g-factor in IMAGEN (F1,1640 = 12.2–94.3; P < 0.005), and up to 3.0% in IntegraMooDS (F1,725 = 10.0–21.0; P < 0.005). The association between polygenic scores and g-factor was partially mediated by SA and CT in prefrontal, anterior cingulate, insula, and medial temporal cortices in both samples (PFWER-corrected < 0.005). The variance explained by mediation was up to 0.75% in IMAGEN and 0.77% in IntegraMooDS. Our results provide evidence that cumulative genetic load influences g-factor via cortical structure. The consistency of our results across samples suggests that cortex morphology could be a novel potential biomarker for neurocognitive dysfunction that is among the most intractable psychiatric symptoms.

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Reis, Cecília, and J. A. Tenreiro Machado. "Computational Intelligence in Circuit Synthesis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (November 20, 2007): 1122–27. http://dx.doi.org/10.20965/jaciii.2007.p1122.

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This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.

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Sachin Kumar Tripathi, Khyati Rao, Rajiv Ratan Singh, and Pradeep kumar Yadav. "Artificial Intelligence and its Role in Forensic Karyotyping: A Systematic Review." Indian Journal of Forensic Medicine & Toxicology 18, no. 1 (January 18, 2024): 114–18. http://dx.doi.org/10.37506/900g7477.

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Introduction: One of the most important aspects of forensic investigations and genetic research is forensickaryotyping, which involves analyzing a person’s chromosomes to find genetic anomalies and establishidentification. The development of artificial intelligence (AI) technology offers a chance to improve and automate the forensic karyotyping procedure. This study examines the possible advantages and difficulties of artificial intelligence (AI) in forensic karyotyping. In forensic science, forensic karyotyping is essential for providing an accurate interpretation of genetic data for use in legal and investigative processes. It can offer useful details regarding genetic problems, such as chromosomal abnormalities or mutations, which can help with personal identification, paternity determination, or supplying proof in criminal investigations.Aim: To give a general review of how artificial intelligence is used in forensic karyotyping, evaluate its possible advantages, and address any relevant issues. By being aware of Artificial Intelligence’s promise and limitations in this field, we may set the stage for its efficient integration into forensic practitioner.Methods: A database search we did to start the inquiry turned up 582 documents. There were 216 unique records left after duplicates were eliminated. 232 items were subsequently eliminated as a result of download problems. A final sample of 31 research was chosen from the 134 full-text papers that were evaluated (n=134), with 103 being eliminated owing to quality issues.Result: The use of artificial intelligence (AI) in forensic karyotyping has several advantages, including automated chromosomal analysis, quicker abnormality discovery, and increased uniformity. For a successful application, challenges such as a lack of labelled datasets and ethical issues must be resolved.Concussion: By increasing productivity, precision, and uniformity, artificial intelligence has the potential totransform forensic karyotyping. While there are obstacles, continued study and cooperation amongst several fields might help you get through them. The ethical and appropriate use of AI in forensic karyotyping will improve forensic investigations, boost genetic research, and expand the use of genetics in the legal system.

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Denkena, Berend, Helge Henning, and Leif-Erik Lorenzen. "Genetics and intelligence: new approaches in production engineering." Production Engineering 4, no. 1 (November 17, 2009): 65–73. http://dx.doi.org/10.1007/s11740-009-0191-z.

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34

Wilkins, Adam S. "Charles Darwin: Genius or Plodder?" Genetics 183, no. 3 (November 2009): 773–77. http://dx.doi.org/10.1534/genetics.109.110452.

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There is no doubt about the magnitude of Charles Darwin's contributions to science. There has, however, been a long-running debate about how brilliant he was. His kind of intelligence was clearly different from that of the great physicists who are deemed geniuses. Here, the nature of Darwin's intelligence is examined in the light of Darwin's actual style of working. Surprisingly, the world of literature and the field of neurobiology might supply more clues to resolving the puzzle than conventional scientific history. Those clues suggest that the apparent discrepancy between Darwin's achievements and his seemingly pedestrian way of thinking reveals nothing to Darwin's discredit but rather a too narrow and inappropriate set of criteria for “genius.” The implications of Darwin's particular creative gifts with respect to the development of scientific genius in general are briefly discussed.

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35

Matuchansky, Claude. "Intelligence clinique et intelligence artificielle." médecine/sciences 35, no. 10 (October 2019): 797–803. http://dx.doi.org/10.1051/medsci/2019158.

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L’intelligence artificielle (IA) actuelle en médecine peut se prévaloir de grandes performances, particulièrement en analyse d’images à visées diagnostique et pronostique, mais, en pratique clinique quotidienne, les résultats de l’IA fondés sur des données probantes restent peu nombreux. Dans cet article, sont analysés les caractéristiques de l’intelligence clinique en pratique médicale, puis les succès et promesses de l’IA, de même que les limites, réserves et critiques apportées à l’introduction de l’IA en clinique de première ligne. Est soulignée l’importance de certains aspects éthiques et de régulation, notamment une « garantie humaine » à l’IA, telle que celle suggérée par le Comité consultatif national d’éthique pour les sciences de la vie et de la santé (CCNE). L’intelligence clinique pourrait être cette garantie humaine de l’IA en médecine, leur complémentarité pouvant conduire à une qualité de décisions largement supérieure à celle fournie séparément par chacune d’elles.

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Nelissen, Jo M. C. "Parent - Child Resemblance: Genetics, Education and Chance." Education and Society 39, no. 2 (December 1, 2021): 29–50. http://dx.doi.org/10.7459/es/39.2.03.

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In this article, it is argued that it makes sense to define and distinguish three levels of human intelligence: intelligence as genotypical potential, intelligence as actualised in environmental interaction, and intelligence as measured by tests (IQ). This raises the questions of what is meant by the term “intelligence as potential”, and how and in what sense does a child’s cognitive potential express the parents’ potential and genetics? The larger the number of genes involved in a certain trait, the more possibilities emerge for the formation of new combinations for that trait. The degree of similarity between a child and their parents depends on the unique combination of innate genes in each newborn child. The more genes are connected with a human trait or ability, the more refined or intricate the structure of the distribution for that trait in a population will be. The question of how a parents’ genes relate to their children’s genes has been studied, among other things, in ‘twin studies’. Another relevant, but complicated question concerns the relation between genetics (nature) and environment (nurture). Nature appears to be at work in nurture, while nurture influences processes of nature. In psychological research, some DNA differences can be used to predict psychological differences, called polygenic scores. In this context, it is argued that individual cognitive growth comes about by all kinds of influences; psychologists call such influences ‘bidirectional’ influences. It is also argued that, ultimately, it is the individual human explorative activity that is responsible and a strong catalyst for the development and mastery of human traits and for the cognitive qualifications of all newborn children.

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Li, Mengjie, Wenting Zhang, and Xiaoyi Zhou. "Identification of genes involved in the evolution of human intelligence through combination of inter-species and intra-species genetic variations." PeerJ 8 (April 16, 2020): e8912. http://dx.doi.org/10.7717/peerj.8912.

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Understanding the evolution of human intelligence is an important undertaking in the science of human genetics. A great deal of biological research has been conducted to search for genes which are related to the significant increase in human brain volume and cerebral cortex complexity during hominid evolution. However, genetic changes affecting intelligence in hominid evolution have remained elusive. We supposed that a subset of intelligence-related genes, which harbored intra-species variations in human populations, may also be evolution-related genes which harbored inter-species variations between humans (Homo sapiens) and great apes (including Pan troglodytes and Pongo abelii). Here we combined inter-species and intra-species genetic variations to discover genes involved in the evolution of human intelligence. Information was collected from published GWAS works on intelligence and a total of 549 genes located within the intelligence-associated loci were identified. The intelligence-related genes containing human-specific variations were detected based on the latest high-quality genome assemblies of three human’s closest species. Finally, we identified 40 strong candidates involved in human intelligence evolution. Expression analysis using RNA-Seq data revealed that most of the genes displayed a relatively high expression in the cerebral cortex. For these genes, there is a distinct expression pattern between humans and other species, especially in neocortex tissues. Our work provided a list of strong candidates for the evolution of human intelligence, and also implied that some intelligence-related genes may undergo inter-species evolution and contain intra-species variation.

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Hwang, Liang-Dar. "Do People with Lower IQ Have Weaker Taste Perception? A Hidden Supplementary Table in ‘Is the Association Between Sweet and Bitter Perception Due to Genetic Variation?’." Twin Research and Human Genetics 23, no. 2 (April 2020): 123–24. http://dx.doi.org/10.1017/thg.2020.19.

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AbstractThis paper is about Nick’s contribution to the field of taste genetics, how I became involved and how a study on the genetic association between the perception of sweetness and bitterness ended up examining the influence of intelligence on taste perception.

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39

Holm, Liisa. "Artificial Intelligence and Molecular Biology." Trends in Genetics 10, no. 6 (June 1994): 216. http://dx.doi.org/10.1016/0168-9525(94)90261-5.

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40

Beaujean, A. Alexander. "Intelligence, Race, and Genetics: Conversations with Arthur R. Jensen." Intelligence 31, no. 1 (January 2003): 93–94. http://dx.doi.org/10.1016/s0160-2896(02)00160-5.

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41

Weiss, Volkmar. "The advent of a molecular genetics of general intelligence." Intelligence 20, no. 2 (March 1995): 115–24. http://dx.doi.org/10.1016/0160-2896(95)90028-4.

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42

Tabery, James. "Why Is Studying the Genetics of Intelligence So Controversial?" Hastings Center Report 45, S1 (September 2015): S9—S14. http://dx.doi.org/10.1002/hast.492.

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Callier, Shawneequa L., and Vence L. Bonham. "Taking a Stand:The Genetics Community's Responsibility for Intelligence Research." Hastings Center Report 45, S1 (September 2015): S54—S58. http://dx.doi.org/10.1002/hast.500.

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Pu, Wen Yu, Yan Nian Rui, Lian Sheng Zhao, and Chun Yan Zhang. "Intelligence Selection System for Honing Parameter Based on Genetics and Artificial Neural Networks." Advanced Materials Research 102-104 (March 2010): 846–50. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.846.

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Appropriate selecting of process parameters influences the machining quality greatly. For honing, the main factors are product precision, material components and productivity. In view of this situation, a intelligence selection model for honing parameter based on genetics and artificial neural networks was built by using excellent robustness, fault-tolerance of artificial neural networks optimization process and excellent self-optimum of genetic algorithm. It can simulate the decision making progress of experienced operators, abstract the relationship from process data and machining incidence, realize the purpose of intelligence selection honing parameter through copying, exchanging, aberrance, replacement strategy and neural networks training. Besides, experiment was performed and the results helped optimize the theories model. Both the theory and experiment show the updated level and feasibility of this system.

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Deary, Ian J. "Intelligence." Current Biology 23, no. 16 (August 2013): R673—R676. http://dx.doi.org/10.1016/j.cub.2013.07.021.

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Mahto, Rajendra Kumar. "Artificial Intelligence for Meiosis and Mitosis Analysis." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 2267–71. http://dx.doi.org/10.52783/jes.1993.

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In cellular biology, meiosis and mitosis are essential processes that control cell division and replication as well as the transfer of genetic material. A thorough understanding of these intricate processes is essential for many fields, such as cancer research, genetics, and developmental biology. In this study, we suggest building a Mitosis and Meiosis Analysis System (MMAS) that uses artificial intelligence (AI) methods to make automated analysis and meiotic event characterization easier. The MMAS uses machine learning models, deep learning frameworks, and sophisticated image processing algorithms to precisely recognize and categorize various meiotic and mitotic stages from microscopy images. The MMAS seeks to increase the accuracy and efficiency of cellular biology research while streamlining the analysis process and minimizing manual labor by utilizing artificial intelligence. Furthermore, by providing insightful information about the dynamic character of mitotic and meiotic events, the MMAS helps scientists understand the underlying mechanisms and their implications for a range of physiological and pathological conditions. We hope to improve our knowledge of meiosis and mitosis and hasten research findings in cellular biology by putting the MMAS into practice.

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Bouchard, Thomas J. "Genes, Evolution and Intelligence." Behavior Genetics 44, no. 6 (March 7, 2014): 549–77. http://dx.doi.org/10.1007/s10519-014-9646-x.

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Nisa, Khoirun, and Deny Setiawan. "PENGEMBANGAN MODUL INTERAKTIF BERBASIS MULTIPLE INTELLIGENCE DENGAN AUTOPLAY PADA KONSEP PERUBAHAN MATERI GENETIK DI IKIP BUDI UTOMO MALANG." Edubiotik : Jurnal Pendidikan, Biologi dan Terapan 3, no. 02 (December 8, 2018): 30–36. http://dx.doi.org/10.33503/ebio.v3i02.168.

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Technology-based learning needs to be done immediately in order to carry out more interested in learning and fostering student motivation. Furthermore, learning should be done byeach student characteristics.One of which is multiple intelligences based learning. Through the application of multiple intelligence assisted technology based learning, it is expected that the learning objectives will be more easily achieved by students. Interactive module based on multiple intelligences that has been validated was the product of this research and developmental experiment. This research was limited to the Changes of Genetic Material taught at the Genetics Course at IKIP Budi Utomo Malang. Research and development model was carried out by 4D model from Thiagarajan. This research phase includes define, design, design, and disseminate stages. The research data is obtained from the module validation score, both from material experts, media experts, and practitioners (students). The data obtained were analyzed qualitatively and quantitatively. Based on the results of the validation of media experts, material experts and small-scale trials, it is known that the percentage results are above 85%, so the product is declared valid. Furthermore, it is necessary to test the classical scale to find out its effectiveness in learning.

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Pagliarini, Luigi. "Polymorphic intelligence." Artificial Life and Robotics 12, no. 1-2 (March 2008): 24–28. http://dx.doi.org/10.1007/s10015-007-0498-9.

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Baughman, Holly M., Julie Aitken Schermer, Livia Veselka, Juliette Harris, and Philip A. Vernon. "A Behavior Genetic Analysis of Trait Emotional Intelligence and Alexithymia: A Replication." Twin Research and Human Genetics 16, no. 2 (January 9, 2013): 554–59. http://dx.doi.org/10.1017/thg.2012.151.

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This replication study examines relations between alexithymia and trait emotional intelligence (trait EI) at the phenotypic, genetic, and environmental levels. A sample of 1,444 same-sex twin pairs (850 MZ pairs and 594 DZ pairs) completed the Toronto Alexithymia Scale-20. A subset of 494 same-sex twin pairs (287 MZ pairs and 207 DZ pairs) had earlier completed the Trait Emotional Intelligence Questionnaire. Individual differences in alexithymia were attributable to genetic, non-shared environmental, and shared environmental factors. All but one of the facets of alexithymia were negatively and significantly correlated with the factors of trait EI, and these phenotypic correlations were entirely attributable to correlated genetic and correlated non-shared environmental factors. These bivariate results provide a valuable replication of those of Baughman et al. (Twin Research and Human Genetics, Vol. 14, 2011, pp. 539–543), which was conducted with substantially smaller samples of twins.

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