000			03725nam a2200337 i 4500
999			_c398869 _d398869
001			398869
003			ES-MaUEC
005			20240314094541.0
006			a|||| o|||| 00| 0
007			cr nn 008mamaa
008			230707s2023 sz | o |||| 0|eng d
020			_a9783031351761
024	7		_a10.1007/978-3-031-35176-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRA776.75 _b2023 EB
245	0	0	_aArtificial Intelligence for Healthy Longevity _cedited by Alexey Moskalev, Ilia Stambler, Alex Zhavoronkov
250			_a1st ed. 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aHealthy Ageing and Longevity _x2199-9015 _v19
505	0		_aAI in longevity -- Automated reporting of medical diagnostic imaging for early disease and aging biomarkers detection -- Risk forecasting tools based on the collected information for two types of occupational diseases -- Obtaining longevity footprints in DNA methylation data using different machine learning approaches -- The role of assistive technology in regulating the behavioural and psychological symptoms of dementia -- Epidemiology, genetics and epigenetics of Biological Aging: one or more aging systems? -- Temporal relation prediction from Electronic Health Records using Graph Neural Networks and Transformers Embeddings -- In silico screening of life-extending drugs using machine learning and omics data -- An overview of kernel methods for identifying genetic association with health-related traits -- Artificial Intelligence approaches for skin anti-aging and skin resilience research -- AI in genomics and epigenomics -- The utility of information theory based methods in the research of aging and longevity -- AI for Longevity: getting past the Mechanical Turk model will take Good Data -- Leveraging algorithmic and human networks to cure human aging: Holistic understanding of Longevity via Generative Cooperative Networks, Hybrid Bayesian/Neural/Logical AI and Tokenomics-Mediated Crowdsourcing. .
520			_aThis book reviews the state-of-the-art efforts to apply machine learning and AI methods for healthy aging and longevity research, diagnosis, and therapy development. The book examines the methods of machine learning and their application in the analysis of big medical data, medical images, the creation of algorithms for assessing biological age, and effectiveness of geroprotective medications. The promises and challenges of using AI to help achieve healthy longevity for the population are manifold. This volume, written by world-leading experts working at the intersection of AI and aging, provides a unique synergy of these two highly prominent fields and aims to create a balanced and comprehensive overview of the application methodology that can help achieve healthy longevity for the population. The book is accessible and valuable for specialists in AI and longevity research, as well as a wide readership, including gerontologists, geriatricians, medical specialists, and students from diverse fields, basic scientists, public and private research entities, and policy makers interested in potential intervention in degenerative aging processes using advanced computational tools. .
988			_aSpringer_BiomedLife_2023
650		7	_2embne _9144609 _aLongevidad
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-35176-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2024 _dz _eb _zSI