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Title: | Towards AI-driven longevity research : an overview | Authors: | Marino, N Putignano, G Cappilli, S Chersoni, E Santuccione, A Calabrese, G Bischof, E Vanhaelen, Q Zhavoronkov, A Scarano, B Mazzotta, AD Santus, E |
Issue Date: | 2023 | Source: | Frontiers in aging, 2023, v. 4, 1057204 | Abstract: | While 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. | Keywords: | Artificial intelligence Machine learning Biomarkers Feature selection Deep aging clock Longevity medicine |
Publisher: | Frontiers Media SA | Journal: | Frontiers in aging | EISSN: | 2673-6217 | DOI: | 10.3389/fragi.2023.1057204 | 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. The 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. |
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