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Towards AI-driven longevity research: An overview

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, re...

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Autores principales: Marino, Nicola, Putignano, Guido, Cappilli, Simone, Chersoni, Emmanuele, Santuccione, Antonella, Calabrese, Giuliana, Bischof, Evelyne, Vanhaelen, Quentin, Zhavoronkov, Alex, Scarano, Bryan, Mazzotta, Alessandro D., Santus, Enrico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018490/
https://www.ncbi.nlm.nih.gov/pubmed/36936271
http://dx.doi.org/10.3389/fragi.2023.1057204
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author Marino, Nicola
Putignano, Guido
Cappilli, Simone
Chersoni, Emmanuele
Santuccione, Antonella
Calabrese, Giuliana
Bischof, Evelyne
Vanhaelen, Quentin
Zhavoronkov, Alex
Scarano, Bryan
Mazzotta, Alessandro D.
Santus, Enrico
author_facet Marino, Nicola
Putignano, Guido
Cappilli, Simone
Chersoni, Emmanuele
Santuccione, Antonella
Calabrese, Giuliana
Bischof, Evelyne
Vanhaelen, Quentin
Zhavoronkov, Alex
Scarano, Bryan
Mazzotta, Alessandro D.
Santus, Enrico
author_sort Marino, Nicola
collection PubMed
description 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.
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spelling pubmed-100184902023-03-17 Towards AI-driven longevity research: An overview Marino, Nicola Putignano, Guido Cappilli, Simone Chersoni, Emmanuele Santuccione, Antonella Calabrese, Giuliana Bischof, Evelyne Vanhaelen, Quentin Zhavoronkov, Alex Scarano, Bryan Mazzotta, Alessandro D. Santus, Enrico Front Aging Aging 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. Frontiers Media S.A. 2023-03-01 /pmc/articles/PMC10018490/ /pubmed/36936271 http://dx.doi.org/10.3389/fragi.2023.1057204 Text en Copyright © 2023 Marino, Putignano, Cappilli, Chersoni, Santuccione, Calabrese, Bischof, Vanhaelen, Zhavoronkov, Scarano, Mazzotta and Santus. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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.
spellingShingle Aging
Marino, Nicola
Putignano, Guido
Cappilli, Simone
Chersoni, Emmanuele
Santuccione, Antonella
Calabrese, Giuliana
Bischof, Evelyne
Vanhaelen, Quentin
Zhavoronkov, Alex
Scarano, Bryan
Mazzotta, Alessandro D.
Santus, Enrico
Towards AI-driven longevity research: An overview
title Towards AI-driven longevity research: An overview
title_full Towards AI-driven longevity research: An overview
title_fullStr Towards AI-driven longevity research: An overview
title_full_unstemmed Towards AI-driven longevity research: An overview
title_short Towards AI-driven longevity research: An overview
title_sort towards ai-driven longevity research: an overview
topic Aging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018490/
https://www.ncbi.nlm.nih.gov/pubmed/36936271
http://dx.doi.org/10.3389/fragi.2023.1057204
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