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迈向人工智能驱动的长寿研究:综述

Towards AI-driven longevity research: An overview.

作者信息

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

机构信息

Women's Brain Project (WBP), Gunterhausen, Switzerland.

Dermatology, Catholic University of the Sacred Heart, Rome, Italy.

出版信息

Front Aging. 2023 Mar 1;4:1057204. doi: 10.3389/fragi.2023.1057204. eCollection 2023.

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.

摘要

过去,技术主要用于存储有关蛋白质和分子结构配置的信息,以用于研究和医学目的,而如今人工智能能够从现有数据中学习如何预测和模拟特性及相互作用,揭示有关诸如衰老等复杂生物过程的重要知识。此外,现代技术可以依靠更广泛的信息集,包括那些来自下一代测序(如蛋白质组学、脂质组学和其他组学)的信息,来理解人体与外部环境之间的相互作用。这一点尤为重要,因为外部因素已被证明在衰老过程中起关键作用。随着计算系统生物学领域不断进步,衰老的新生物标志物不断涌现,人工智能有望成为衰老研究的重要助力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8600/10018490/dec8c41939b5/fragi-04-1057204-g001.jpg

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