Towards AI-driven longevity research: An overview

Nicola Marino, Guido Putignano, Simone Cappili, Emmanuele Chersoni, Antonella Santuccione, Giuliana Calabrese, Evelyne Bischof, Quentin Vanhaelen, Alex Zhavoronkov, Bryan Scarano, Alessandro D. Mazzotta, Enrico Santus

Research output: Journal article publicationReview articleAcademic researchpeer-review

12 Citations (Scopus)

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.
Original languageEnglish
Article number1057204
JournalFrontiers in Aging
Volume4
DOIs
Publication statusPublished - 1 Mar 2023

Keywords

  • artificial intelligence
  • machine learning
  • biomarkers
  • feature selection
  • deep aging clock
  • longevity medicine

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