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Artykuły w czasopismach na temat "Intelligence – genetics"
Farzan, Raed. "Artificial intelligence in Immuno-genetics". Bioinformation 20, nr 1 (31.01.2024): 29–35. http://dx.doi.org/10.6026/973206300200029.
Pełny tekst źródłaBARNETT, S. A. "GENETICS OF INTELLIGENCE". Developmental Medicine & Child Neurology 5, nr 5 (12.11.2008): 522. http://dx.doi.org/10.1111/j.1469-8749.1963.tb10712.x.
Pełny tekst źródłaDeary, Ian J., Frank M. Spinath i Timothy C. Bates. "Genetics of intelligence". European Journal of Human Genetics 14, nr 6 (24.05.2006): 690–700. http://dx.doi.org/10.1038/sj.ejhg.5201588.
Pełny tekst źródłaPlomin, Robert, i Jenae Neiderhiser. "Quantitative Genetics, Molecular Genetics, and Intelligence". Intelligence 15, nr 4 (październik 1991): 369–87. http://dx.doi.org/10.1016/0160-2896(91)90001-t.
Pełny tekst źródłaSternberg, Robert J., Elena L. Grigorenko i Kenneth K. Kidd. "Intelligence, race, and genetics." American Psychologist 60, nr 1 (2005): 46–59. http://dx.doi.org/10.1037/0003-066x.60.1.46.
Pełny tekst źródłaVASYLKIVSKYI, Mikola, Ganna VARGATYUK i Olga BOLDYREVA. "INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE". Herald of Khmelnytskyi National University. Technical sciences 217, nr 1 (23.02.2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.
Pełny tekst źródłaKrittanawong, Chayakrit, Kipp W. Johnson, Edward Choi, Scott Kaplin, Eric Venner, Mullai Murugan, Zhen Wang i in. "Artificial Intelligence and Cardiovascular Genetics". Life 12, nr 2 (14.02.2022): 279. http://dx.doi.org/10.3390/life12020279.
Pełny tekst źródłaPlomin, Robert, i Frank M. Spinath. "Intelligence: Genetics, Genes, and Genomics." Journal of Personality and Social Psychology 86, nr 1 (styczeń 2004): 112–29. http://dx.doi.org/10.1037/0022-3514.86.1.112.
Pełny tekst źródłaPlomin, Robert, i Sophie von Stumm. "The new genetics of intelligence". Nature Reviews Genetics 19, nr 3 (8.01.2018): 148–59. http://dx.doi.org/10.1038/nrg.2017.104.
Pełny tekst źródłaPlomin, Robert, i Stephen A. Petrill. "Genetics and intelligence: What's new?" Intelligence 24, nr 1 (styczeń 1997): 53–77. http://dx.doi.org/10.1016/s0160-2896(97)90013-1.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaPrabhakar, 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.
Pełny tekst źródłaOng, 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.
Pełny tekst źródłaHepatocellular 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.
Pełny tekst źródłaTakane, 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.
Pełny tekst źródłaСмірнов, Олег Ювеналійович, Олег Ювенальевич Смирнов i Oleh Yuvenaliiovych Smirnov. "Генетична складова інтелекту". Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/14286.
Pełny tekst źródłaYeo, Ronald A., Sephira G. Ryman, den Heuvel Martijn P. van, Reus Marcel A. de, Rex E. Jung, Jessica Pommy, Andrew R. Mayer i in. "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.
Pełny tekst źródłaPenke, 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.
Pełny tekst źródłaAfter 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.
Pełny tekst źródłaDepartment 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.
Pełny tekst źródłaSkärmaktivitet 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.
Książki na temat "Intelligence – genetics"
Weiss, Volkmar. Psychogenetik der Intelligenz = Psychogenetics of intelligence. Dortmund: Verlag Modernes Lernen, 1986.
Znajdź pełny tekst źródła1959-, Roleff Tamara L., red. Genetics and intelligence. San Diego, CA: Greenhaven Press, 1996.
Znajdź pełny tekst źródłaKate, Webb, Goode Jamie, Bock Gregory i Novartis Foundation, red. The nature of intelligence. Chichester, West Sussex, England: John Wiley & Sons, 2000.
Znajdź pełny tekst źródłaFrank, Miele, red. Intelligence, race, and genetics: Conversations with Arthur R. Jensen. Boulder, Colo: Westview Press, 2002.
Znajdź pełny tekst źródłaAlan, McGregor, red. Evolution, creative intelligence, and intergroup competition. Washington, D.C: Cliveden Press, 1986.
Znajdź pełny tekst źródłaPearson, Roger. Race, intelligence, and bias in academe. Washington, D.C: Scott-Townsend Publishers, 1991.
Znajdź pełny tekst źródłaR, Koza John, red. Genetic programming IV: Routine human-competitive machine intelligence. Norwell, Mass: Kluwer Academic Publishers, 2003.
Znajdź pełny tekst źródłaStorfer, Miles D. Intelligence and giftedness: The contributions of heredity and early environment. San Francisco: Jossey-Bass Publishers, 1990.
Znajdź pełny tekst źródłaShenk, David. The genius in all of us: Why everything you've been told about genetics, talent, and IQ is wrong. New York: Doubleday, 2010.
Znajdź pełny tekst źródłaPress, Greenhaven. Genetics and Intelligence. Bt Bound, 1999.
Znajdź pełny tekst źródłaCzęści książek na temat "Intelligence – genetics"
Brody, Nathan. "Intelligence and the behavioral genetics of personality." W Nature, nurture & psychology., 161–78. Washington: American Psychological Association, 1993. http://dx.doi.org/10.1037/10131-007.
Pełny tekst źródłaMcGuffin, Peter. "The Quantitative and Molecular Genetics of Human Intelligence". W The Nature of Intelligence, 243–59. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/0470870850.ch15.
Pełny tekst źródłaKreinovich, Vladik, i Max Shpak. "Decomposable Aggregability in Population Genetics and Evolutionary Computations: Algorithms and Computational Complexity". W 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.
Pełny tekst źródłaGialluisi, Alessandro, Benedetta Izzi, Giovanni de Gaetano i Licia Iacoviello. "Epidemiology, Genetics and Epigenetics of Biological Aging: One or More Aging Systems?" W Artificial Intelligence for Healthy Longevity, 115–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35176-1_6.
Pełny tekst źródłaHe, Bing, Neel Sangani, Ruiming Wu, Pradeep Varathan, Alice Patania, Shannon L. Risacher, Kwangsik Nho i in. "Integrative Analysis of Amyloid Imaging and Genetics Reveals Subtypes of Alzheimer Progression in Early Stage". W Artificial Intelligence in Medicine, 204–11. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66535-6_23.
Pełny tekst źródłaYang, Qifan, Sophia I. Thomopoulos, Linda Ding, Wesley Surento, Paul M. Thompson i Neda Jahanshad. "Support Vector Based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimer’s Disease". W Predictive Intelligence in Medicine, 160–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32281-6_17.
Pełny tekst źródłaCrow, T. J. "A Single Locus for Psychosis and Intelligence in the Exchange Region of the Sex Chromosomes?" W 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.
Pełny tekst źródłaLaazaar, Kaoutar, i Noureddine Boutammachte. "Modelling and Optimization of Stirling Engine for Waste Heat Recovery from Cement Plant Based on Adiabatic Model and Genetics Algorithms". W Artificial Intelligence and Industrial Applications, 287–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53970-2_27.
Pełny tekst źródłaChiang, Ming-Chang, Marina Barysheva, Agatha D. Lee, Sarah Madsen, Andrea D. Klunder, Arthur W. Toga, Katie L. McMahon i in. "Brain Fiber Architecture, Genetics, and Intelligence: A High Angular Resolution Diffusion Imaging (HARDI) Study". W 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.
Pełny tekst źródłaHazotte, Cyril, Hélène Mayer, Younes Djaghloul, Thibaud Latour, Philipp Sonnleitner, Martin Brunner, Ulrich Keller, Eric Francois i Romain Martin. "The Genetics Lab: An Innovative Tool for Assessment of Intelligence by Mean of Complex Problem Solving". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Intelligence – genetics"
Matthews, D., T. Pabiou, R. D. Evans, C. Beder i A. Daly. "129. Predicting carcass cut yields in cattle from digital images using artificial intelligence". W 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.
Pełny tekst źródłaBudiarto, Arif, i Bens Pardamean. "Explainable Supervised Method for Genetics Ancestry Estimation". W 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI). IEEE, 2021. http://dx.doi.org/10.1109/iccsai53272.2021.9609748.
Pełny tekst źródłaAnguera, Jaume, Aurora Andujar, Jeevani Jayasinghe, Disala Uduwawala, Muhammad K. Khattak i Sungtek Kahng. "Nature-Inspired High-Directivity Microstrip Antennas: Fractals and Genetics". W 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2016. http://dx.doi.org/10.1109/cicn.2016.46.
Pełny tekst źródłaChandra, Mukesh, Pallavi Somvanshi, B. N. Mishra i Amod Tiwari. "Genetics of Yellow Mosaic Virus Resistance in Mung bean". W 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705760.
Pełny tekst źródłaLi, Chang, Mingyong Hu i Xing Han. "Reliability Optimum Design for Bevel Gear Driven Systems Based on Genetics Algorithm". W 2016 International Conference on Artificial Intelligence and Engineering Applications. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/aiea-16.2016.66.
Pełny tekst źródłaBie, Lin, Rui Zhang i Zhiteng Wang. "Quantum genetics clustering algorithm based on high-Dimensional and multi-chain coding scheme". W 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI ). IEEE, 2018. http://dx.doi.org/10.1109/icaci.2018.8377478.
Pełny tekst źródłaAhmed, Zahid, Subbulakshmi Ganesan i Rashmi Tirkey. "Understanding the Potential of Grouping Algorithms for Genetics Clustering". W 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.
Pełny tekst źródłaChen, Minmin, Yixin Chen, Michael R. Brent i Aaron E. Tenney. "Gradient-Based Feature Selection for Conditional Random Fields and its Applications in Computational Genetics". W 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.82.
Pełny tekst źródłaAswinseshadri, K., i V. Thulasi Bai. "Feature Selection in Brain Computer Interface Using Genetics Method". W 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.
Pełny tekst źródłaNojima, Yusuke, Kazuhiro Watanabe i Hisao Ishibuchi. "Variants of heuristic rule generation from multiple patterns in Michigan-style fuzzy genetics-based machine learning". W 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2015. http://dx.doi.org/10.1109/taai.2015.7407091.
Pełny tekst źródłaRaporty organizacyjne na temat "Intelligence – genetics"
Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), wrzesień 1996. http://dx.doi.org/10.55274/r0010423.
Pełny tekst źródłaTarozzi, Martina Elena. Next Generation Sequencing Technologies, Bioinformatics and Artificial Intelligence: A Shared Time-line. MZB Standard Enterprise, lipiec 2024. http://dx.doi.org/10.57098/scirevs.biology.3.2.2.
Pełny tekst źródłaPERFORMANCE OPTIMIZATION OF A STEEL-UHPC COMPOSITE ORTHOTROPIC BRIDGE WITH INTELLIGENT ALGORITHM. The Hong Kong Institute of Steel Construction, sierpień 2022. http://dx.doi.org/10.18057/icass2020.p.160.
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