Academic literature on the topic 'Intelligence – genetics'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Intelligence – genetics.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Intelligence – genetics"
Farzan, Raed. "Artificial intelligence in Immuno-genetics." Bioinformation 20, no. 1 (January 31, 2024): 29–35. http://dx.doi.org/10.6026/973206300200029.
Full textBARNETT, 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.
Full textDeary, 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.
Full textPlomin, 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.
Full textSternberg, 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.
Full textVASYLKIVSKYI, 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.
Full textKrittanawong, 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.
Full textPlomin, 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.
Full textPlomin, 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.
Full textPlomin, 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.
Full textDissertations / Theses on the topic "Intelligence – genetics"
Avgan, Nesli. "The genetic basis of human cognition: Intelligence, learning and memory." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/122903/1/Nesli_Avgan_Thesis.pdf.
Full textPrabhakar, Nachiketh. "Deep Learning To Improve Hi-C Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1562582435975614.
Full textOng, Vy Quoc. "Subgroup Analysis of Patients with Hepatocellular Carcinoma| A Quest for Statistical Algorithms for Tissue Classification Problem." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10840510.
Full textHepatocellular carcinoma (HCC) is the most common type of liver cancer. This type of cancer has been observed with prevalence as the third leading cause of death from cancer worldwide and as the ninth leading cause of cancerous mortality in the United States. People with hepatitis B or C are considered to be at high risk for this kind of cancer. Remarkably poor prognostic HCC patients with low survival rates commonly possess intra-hepatic metastases that are either tumor thrombi in the portal vein or intra-hepatic spread. It is uncommon for them to die of extra-hepatic metastases. Therefore, identifying metastatic HCC has become vital and clinically challenging in efforts of timely therapeutic intervention to improve the survival rate of patients who suffer from this disease.
To date, studies that look for an accurate molecular profiling model have been developed to identify these patients in advance for a better treatment or intervention. An approach has been to focus on identifying individual candidate genes characterizing metastatic HCC. Another direction has been to find a global genome scale solution by using microarray technology to obtain a gene expression for this carcinoma. Among research following the latter was that developed by Qing-Hai Ye et al., Nature Medicine, Volume 9, Number 4, April 2003. They applied cDNA microarray-based gene expression profiling with compound co-variate predictors for primary HCC, metastatic HCC, and metastasis-free HCC binary classification tasks on a dataset of 87 observations and 9984 features taken from 40 hepatitis B-positive Chinese patients. Notably, a robust 153-gene model was generated to successfully classify tumor-thrombi-in-the-portal-vein samples with metastasis-free samples. However, they admitted distinguishing primary tumor samples from their matched-metastatic lesions were still a challenge. In this molecule signature, a gene named osteopontin, a secreted phosphoprotein, served as the lead gene in diagnosing HCC metastasis.
The analysis is based on the metastatic status of HCC, which is clinically predetermined. However, the validation of the class definition is needed to investigate if the data are sufficient to translate the three classes predefined. We will use some statistical clustering algorithms to validate the class defined. After that, we will conduct variable selection to find markers that are differentially expressed genes among clinical groups validated from this research. Next, using the compound markers found by this research, we will develop a statistical model to predict a new patient’s HCC type for intervention. The generalized performance of the prediction model will be evaluated via a cross-validation test. This study aims to build a highly accurate model that renders a better classification of the fore-mentioned clinical groups of HCC and thus enhances the rate of predicting metastatic patients.
Beiko, Robert G. "Evolutionary computing strategies for the detection of conserved patterns in genomic DNA." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/29009.
Full textTakane, Marina. "Inference of gene regulatory networks from large scale gene expression data." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80883.
Full textСмірнов, Олег Ювеналійович, Олег Ювенальевич Смирнов, and Oleh Yuvenaliiovych Smirnov. "Генетична складова інтелекту." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/14286.
Full textYeo, Ronald A., Sephira G. Ryman, den Heuvel Martijn P. van, Reus Marcel A. de, Rex E. Jung, Jessica Pommy, Andrew R. Mayer, et al. "Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics." Cambridge University Press, 2016. https://tud.qucosa.de/id/qucosa%3A70691.
Full textPenke, Lars. "Neuroscientific approaches to general intelligence and cognitive ageing." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/13979.
Full textAfter an extensive review of what is known about the genetics and neuroscience of general intelligence and a methodological note emphasising the necessity to consider latent variables in cognitive neuroscience studies, exemplified by a re-analysis of published results, the most well-established brain correlate of intelligence, brain size, is revisited from an evolutionary genetic perspective. Estimates of the coefficient of additive genetic variation in brain size suggest that there was no recent directional selection on brain size, questioning its validity as a proxy for intelligence in evolutionary analyses. Instead, correlations of facial fluctuating asymmetry with intelligence and information processing speed in old men suggest that organism-wide developmental stability might be an important cause of individual differences in cognitive ability. The second half of the thesis focuses on cognitive ageing, beginning with a general review. In a sample of over 130 subjects it has then been found that the integrity of different white matter tracts in the brain is highly correlated, allowing for the extraction of a general factor of white matter tract integrity, which is correlated with information processing speed. The only tract not loading highly on this general factor is the splenium of the corpus callosum, which is correlated with changes in intelligence over 6 decades and mediates the effect of the beta2 adrenergic receptor gene (ADRB2) on cognitive ageing, possibly due to its involvement in neuronal compensation processes. Finally, using a novel analytic method for magnetic resonance data, it is shown that more iron depositions in the brain, presumably markers of a history of cerebral microbleeds, are associated with both lifelong-stable intelligence differences and age-related decline in cognitive functioning.
Horstman, Benjamin Philip. "Detecting Epistasis Effect in Genome-Wide Association Studies Based on Permutation Tests and Ensemble Approaches." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1270577390.
Full textDepartment of EECS - Computer and Information Sciences Title from PDF (viewed on 2010-05-25) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center
Kravchenko, Evgenija. "Association between cognitive measures, global brain surface area, genetics, and screen-time in young adolescents : Estimation of causal inference with machine learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290033.
Full textSkärmaktivitet som att titta på TV och video, spela videospel och använda sociala medier har blivit en populär fritidsaktivitet för barn och ungdomar. Effekten av skärmtid har varit ett mycket debatterat ämne; det finns dock fortfarande mycket lite kunskap om det. Med hjälp av datasetet från Adolescent Brain Cognitive Development långtidsstudien kunde 4 217 ungdomar, som uppfyllde specifika krav, väljas ut för detta avhandlingsprojekt efter bearbetning av datan. Detta avhandlingsprojekt undersökte kausal ordning mellan genetisk effekt (Polygenic scores (PGS) för kognitiv prestation), skärmtidsaktivitet, hjärnmorfologi (strukturell Magnet Resonans Imaging (sMRI) för hjärnans ytarea och hjärnbarks tjocklek), brist på ihärdighet och kognitiv förmåga (kristalliserad IQ) med en maskininlärningsalgoritm DirectLiNGAM. Tydlig korrelation mellan skärmaktivitet och PGS hittades för alla typer av skärmaktiviteter men endast videospel och sociala medier korrelerade till den globala ytarean. Dessutom verkar TV och video påverka brist på ihärdighet och brist på ihärdighet i sin tur påverkar hur mycket tid som spenderas på videospel. Dessa resultat antyder att olika typer av sociala medier inte är så lika som vi trodde och kan påverka ungdomar olika. Sammanlagt stöder dessa upptäckter tidigare forskning om skärmtidseffekt på brist på ihärdighet, hjärnmorfologi och kognitiv förmåga och föreslår en ny kausal inferens mellan genetik och skärmtid. Slutligen ledde algoritmen som användes i detta avhandlingsprojekt fram till rimliga kausala ordningar och kan ses som ett mycket bra komplement till dagens kausala modellering.
Books on the topic "Intelligence – genetics"
Weiss, Volkmar. Psychogenetik der Intelligenz = Psychogenetics of intelligence. Dortmund: Verlag Modernes Lernen, 1986.
Find full text1959-, Roleff Tamara L., ed. Genetics and intelligence. San Diego, CA: Greenhaven Press, 1996.
Find full textKate, Webb, Goode Jamie, Bock Gregory, and Novartis Foundation, eds. The nature of intelligence. Chichester, West Sussex, England: John Wiley & Sons, 2000.
Find full textJensen, Arthur Robert. Intelligence, race, and genetics: Conversations with Arthur R. Jensen. Boulder, Colo: Westview Press, 2002.
Find full textAlan, McGregor, ed. Evolution, creative intelligence, and intergroup competition. Washington, D.C: Cliveden Press, 1986.
Find full textPearson, Roger. Race, intelligence, and bias in academe. Washington, D.C: Scott-Townsend Publishers, 1991.
Find full textR, Koza John, ed. Genetic programming IV: Routine human-competitive machine intelligence. Norwell, Mass: Kluwer Academic Publishers, 2003.
Find full textR, Stephens Christopher, ed. Foundations of genetic algorithms: 9th international workshop, FOGA 2007, Mexico City, Mexico, January 8-11, 2007 : revised selected papers. Berlin: Springer, 2007.
Find full textS, Gazzaniga Michael. Nature's mind: The biological roots of thinking, emotions, sexuality, language, and intelligence. New York: BasicBooks, 1992.
Find full textStorfer, Miles D. Intelligence and giftedness: The contributions of heredity and early environment. San Francisco: Jossey-Bass Publishers, 1990.
Find full textBook chapters on the topic "Intelligence – genetics"
Brody, Nathan. "Intelligence and the behavioral genetics of personality." In Nature, nurture & psychology., 161–78. Washington: American Psychological Association, 1993. http://dx.doi.org/10.1037/10131-007.
Full textMcGuffin, Peter. "The Quantitative and Molecular Genetics of Human Intelligence." In The Nature of Intelligence, 243–59. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/0470870850.ch15.
Full textKreinovich, Vladik, and Max Shpak. "Decomposable Aggregability in Population Genetics and Evolutionary Computations: Algorithms and Computational Complexity." In Computational Intelligence in Medical Informatics, 69–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75767-2_4.
Full textGialluisi, Alessandro, Benedetta Izzi, Giovanni de Gaetano, and Licia Iacoviello. "Epidemiology, Genetics and Epigenetics of Biological Aging: One or More Aging Systems?" In Artificial Intelligence for Healthy Longevity, 115–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35176-1_6.
Full textYang, Qifan, Sophia I. Thomopoulos, Linda Ding, Wesley Surento, Paul M. Thompson, and Neda Jahanshad. "Support Vector Based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimer’s Disease." In Predictive Intelligence in Medicine, 160–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32281-6_17.
Full textCrow, T. J. "A Single Locus for Psychosis and Intelligence in the Exchange Region of the Sex Chromosomes?" In Ethical Issues of Molecular Genetics in Psychiatry, 12–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76429-5_2.
Full textLaazaar, Kaoutar, and Noureddine Boutammachte. "Modelling and Optimization of Stirling Engine for Waste Heat Recovery from Cement Plant Based on Adiabatic Model and Genetics Algorithms." In Artificial Intelligence and Industrial Applications, 287–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53970-2_27.
Full textChiang, Ming-Chang, Marina Barysheva, Agatha D. Lee, Sarah Madsen, Andrea D. Klunder, Arthur W. Toga, Katie L. McMahon, et al. "Brain Fiber Architecture, Genetics, and Intelligence: A High Angular Resolution Diffusion Imaging (HARDI) Study." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 1060–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_126.
Full textHazotte, Cyril, Hélène Mayer, Younes Djaghloul, Thibaud Latour, Philipp Sonnleitner, Martin Brunner, Ulrich Keller, Eric Francois, and Romain Martin. "The Genetics Lab: An Innovative Tool for Assessment of Intelligence by Mean of Complex Problem Solving." In Informatics Engineering and Information Science, 296–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25483-3_24.
Full textHedjazi, Seyyed Mahdi, and Samane Sadat Marjani. "Pruned Genetic Algorithm." In Artificial Intelligence and Computational Intelligence, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16527-6_25.
Full textConference papers on the topic "Intelligence – genetics"
Matthews, D., T. Pabiou, R. D. Evans, C. Beder, and A. Daly. "129. Predicting carcass cut yields in cattle from digital images using artificial intelligence." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_129.
Full textBudiarto, Arif, and Bens Pardamean. "Explainable Supervised Method for Genetics Ancestry Estimation." In 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI). IEEE, 2021. http://dx.doi.org/10.1109/iccsai53272.2021.9609748.
Full textAnguera, Jaume, Aurora Andujar, Jeevani Jayasinghe, Disala Uduwawala, Muhammad K. Khattak, and Sungtek Kahng. "Nature-Inspired High-Directivity Microstrip Antennas: Fractals and Genetics." In 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2016. http://dx.doi.org/10.1109/cicn.2016.46.
Full textChandra, Mukesh, Pallavi Somvanshi, B. N. Mishra, and Amod Tiwari. "Genetics of Yellow Mosaic Virus Resistance in Mung bean." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705760.
Full textLi, Chang, Mingyong Hu, and Xing Han. "Reliability Optimum Design for Bevel Gear Driven Systems Based on Genetics Algorithm." In 2016 International Conference on Artificial Intelligence and Engineering Applications. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/aiea-16.2016.66.
Full textBie, Lin, Rui Zhang, and Zhiteng Wang. "Quantum genetics clustering algorithm based on high-Dimensional and multi-chain coding scheme." In 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI ). IEEE, 2018. http://dx.doi.org/10.1109/icaci.2018.8377478.
Full textAhmed, Zahid, Subbulakshmi Ganesan, and Rashmi Tirkey. "Understanding the Potential of Grouping Algorithms for Genetics Clustering." In 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA). IEEE, 2024. http://dx.doi.org/10.1109/aimla59606.2024.10531608.
Full textChen, Minmin, Yixin Chen, Michael R. Brent, and Aaron E. Tenney. "Gradient-Based Feature Selection for Conditional Random Fields and its Applications in Computational Genetics." In 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.82.
Full textAswinseshadri, K., and V. Thulasi Bai. "Feature Selection in Brain Computer Interface Using Genetics Method." In 2015 IEEE International Conferences on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; and Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE, 2015. http://dx.doi.org/10.1109/cit/iucc/dasc/picom.2015.39.
Full textNojima, Yusuke, Kazuhiro Watanabe, and Hisao Ishibuchi. "Variants of heuristic rule generation from multiple patterns in Michigan-style fuzzy genetics-based machine learning." In 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2015. http://dx.doi.org/10.1109/taai.2015.7407091.
Full textReports on the topic "Intelligence – genetics"
Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 1996. http://dx.doi.org/10.55274/r0010423.
Full textTarozzi, Martina Elena. Next Generation Sequencing Technologies, Bioinformatics and Artificial Intelligence: A Shared Time-line. MZB Standard Enterprise, July 2024. http://dx.doi.org/10.57098/scirevs.biology.3.2.2.
Full textPERFORMANCE OPTIMIZATION OF A STEEL-UHPC COMPOSITE ORTHOTROPIC BRIDGE WITH INTELLIGENT ALGORITHM. The Hong Kong Institute of Steel Construction, August 2022. http://dx.doi.org/10.18057/icass2020.p.160.
Full text