Artificial Intelligence based approaches to identify molecular determinants of exceptional health and life span
- 1National Institutes of Health (NIH), United States
- 2University of Pennsylvania, United States
Artificial intelligence (AI) has emerged as a powerful approach for integrated analysis of the rapidly growing volume of multi-omics data, predicting disease risk, identification of potential therapeutic targets and many other research and clinical tasks. However, the potential for AI to facilitate the identification of factors contributing to human health and life span and their translation into novel interventions for healthy aging remains to be fully exploited. As researchers on aging acquire large scale data both in human cohorts and model organisms, emerging opportunities exist for the application of AI approaches to untangle the complex physiologic process(es) that modulate health and life span. It is expected that efficient and novel data mining tools that could unravel molecular mechanisms and causal pathways associated with exceptional health and life span could accelerate the discovery of novel therapeutics for healthy aging. Keeping this in mind, the National Institute on Aging (NIA) convened an interdisciplinary workshop titled “Contributions of Artificial Intelligence to Research on Determinants and Modulation of Health Span and Life Span” in August 2018. The workshop involved experts in the fields of aging, comparative biology, cardiology, cancer, and computational science/AI to brainstorm ideas on how AI can be leveraged for the analyses of large-scale data sets from human epidemiological studies and animal/model organisms to close the current knowledge gaps in processes that drive exceptional longevity. This report summarizes the discussions and recommendations from the workshop on future application of AI approaches to advance our understanding of human health and life span.
Keywords: artificial intelligence, Health and life span, GWAS, Protective factors, systems approach, Target identification, machine learning, deep learning, comparative biology
Received: 02 Apr 2019;
Accepted: 08 Jul 2019.
Edited by:Enrico Capobianco, University of Miami, United States
Reviewed by:Marian Beekman, Leiden University Medical Center, Netherlands
Laura I. Furlong, Mar Institute of Medical Research (IMIM), Spain
Copyright: © 2019 Raghavachari and Moore. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Nalini Raghavachari, National Institutes of Health (NIH), Bethesda, United States, firstname.lastname@example.org