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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Vydané v:Frontiers in aging Ročník 4; s. 1057204
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Media S.A 01.03.2023
Predmet:
Aging
artificial intelligence
biomarkers
deep aging clock
feature selection
longevity medicine
machine learning
deep aging clock
machine learning
biomarkers
longevity medicine
artificial intelligence
feature selection
ISSN:2673-6217, 2673-6217
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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.
AbstractList 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.
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.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.
Author Zhavoronkov, Alex
Putignano, Guido
Chersoni, Emmanuele
Marino, Nicola
Cappilli, Simone
Mazzotta, Alessandro D.
Santuccione, Antonella
Vanhaelen, Quentin
Santus, Enrico
Calabrese, Giuliana
Bischof, Evelyne
Scarano, Bryan
AuthorAffiliation 2 Dermatology, Catholic University of the Sacred Heart , Rome , Italy
8 Biorobotics Institute, Scuola Superiore Sant’anna , Pisa , Italy
9 Bayer USA , New Jersey , United States
3 UOC of Dermatology, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, A. Gemelli University Hospital Foundation-IRCCS , Rome , Italy
7 Department of Digestive, Oncological and Metabolic Surgery, Institute Mutualiste Montsouris , Paris , France
5 Department of Translational Medicine and Surgery, CatholicUniversity of the Sacred Heart , Rome , Italy
1 Women’s Brain Project (WBP) , Gunterhausen , Switzerland
4 Department of Chinese and Bilingual Studies , The Hong Kong Polytechnic University , Hong Kong , China
6 Insilico Medicine Hong Kong Ltd. , New Territories , Hong Kong SAR , China
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Copyright © 2023 Marino, Putignano, Cappilli, Chersoni, Santuccione, Calabrese, Bischof, Vanhaelen, Zhavoronkov, Scarano, Mazzotta and Santus. 2023 Marino, Putignano, Cappilli, Chersoni, Santuccione, Calabrese, Bischof, Vanhaelen, Zhavoronkov, Scarano, Mazzotta and Santus
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Keywords deep aging clock
machine learning
biomarkers
longevity medicine
artificial intelligence
feature selection
Language English
License Copyright © 2023 Marino, Putignano, Cappilli, Chersoni, Santuccione, Calabrese, Bischof, Vanhaelen, Zhavoronkov, Scarano, Mazzotta and Santus.
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Guang Yang, Imperial College London, United Kingdom
These authors have contributed equally to this work
Reviewed by: Mark A. McCormick, University of New Mexico, United States
This article was submitted to Interventions in Aging, a section of the journal Frontiers in Aging
Edited by: Brenna Osborne, University of Copenhagen, Denmark
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SubjectTerms Aging
artificial intelligence
biomarkers
deep aging clock
feature selection
longevity medicine
machine learning
Title Towards AI-driven longevity research: An overview
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