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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Veröffentlicht in:Frontiers in aging Jg. 4; S. 1057204
Hauptverfasser: 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
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 01.03.2023
Schlagworte:
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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Zusammenfassung: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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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
ISSN:2673-6217
2673-6217
DOI:10.3389/fragi.2023.1057204