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 TITLE=Towards AI-driven longevity research: An overview JOURNAL=Frontiers in Aging VOLUME=4 YEAR=2023 URL=https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2023.1057204 DOI=10.3389/fragi.2023.1057204 ISSN=2673-6217 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.