Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/106214
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dc.contributorDepartment of Chinese and Bilingual Studiesen_US
dc.creatorMarino, Nen_US
dc.creatorPutignano, Gen_US
dc.creatorCappilli, Sen_US
dc.creatorChersoni, Een_US
dc.creatorSantuccione, Aen_US
dc.creatorCalabrese, Gen_US
dc.creatorBischof, Een_US
dc.creatorVanhaelen, Qen_US
dc.creatorZhavoronkov, Aen_US
dc.creatorScarano, Ben_US
dc.creatorMazzotta, ADen_US
dc.creatorSantus, Een_US
dc.date.accessioned2024-05-03T00:45:49Z-
dc.date.available2024-05-03T00:45:49Z-
dc.identifier.urihttp://hdl.handle.net/10397/106214-
dc.language.isoenen_US
dc.publisherFrontiers Media SAen_US
dc.rights© 2023 Marino, Putignano, Cappilli, Chersoni, Santuccione, Calabrese, Bischof, Vanhaelen, Zhavoronkov, Scarano, Mazzotta and Santus. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rightsThe following publication Marino N, Putignano G, Cappilli S, Chersoni E, Santuccione A, Calabrese G, Bischof E, Vanhaelen Q, Zhavoronkov A, Scarano B, Mazzotta AD and Santus E (2023) Towards AI-driven longevity research: An overview. Front. Aging 4:1057204 is available at https://dx.doi.org/10.3389/fragi.2023.1057204.en_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectBiomarkersen_US
dc.subjectFeature selectionen_US
dc.subjectDeep aging clocken_US
dc.subjectLongevity medicineen_US
dc.titleTowards AI-driven longevity research : an overviewen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume4en_US
dc.identifier.doi10.3389/fragi.2023.1057204en_US
dcterms.abstractWhile in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationFrontiers in aging, 2023, v. 4, 1057204en_US
dcterms.isPartOfFrontiers in agingen_US
dcterms.issued2023-
dc.identifier.isiWOS:001087789700001-
dc.identifier.eissn2673-6217en_US
dc.identifier.artn1057204en_US
dc.description.validate202405 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOS-
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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