Literature DB >> 33733101

Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging.

Jason H Moore1, Nalini Raghavachari2.   

Abstract

Artificial intelligence (AI) has emerged as a powerful approach for integrated analysis of the rapidly growing volume of multi-omics data, including many research and clinical tasks such as prediction of disease risk and identification of potential therapeutic targets. However, the potential for AI to facilitate the identification of factors contributing to human exceptional health and life span and their translation into novel interventions for enhancing health and life span has not yet been realized. 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 who brainstormed 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 life and health span. 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.
Copyright © 2019 Moore, Raghavachari and Workshop Speakers.

Entities:  

Keywords:  GWAS; artificial intelligence; deep learning, comparative biology; health span and life span; machine learning; protective factors; systems approach

Year:  2019        PMID: 33733101      PMCID: PMC7861312          DOI: 10.3389/frai.2019.00012

Source DB:  PubMed          Journal:  Front Artif Intell        ISSN: 2624-8212


  77 in total

Review 1.  Limitations and risks of meta-analyses of longevity studies.

Authors:  Paola Sebastiani; Harold Bae; Anastasia Gurinovich; Mette Soerensen; Annibale Puca; Thomas T Perls
Journal:  Mech Ageing Dev       Date:  2017-01-28       Impact factor: 5.432

2.  Four Genome-Wide Association Studies Identify New Extreme Longevity Variants.

Authors:  Paola Sebastiani; Anastasia Gurinovich; Harold Bae; Stacy Andersen; Alberto Malovini; Gil Atzmon; Francesco Villa; Aldi T Kraja; Danny Ben-Avraham; Nir Barzilai; Annibale Puca; Thomas T Perls
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2017-10-12       Impact factor: 6.053

3.  THE FUTURIST Toward a personalized, learning healthcare system.

Authors:  Eric Topol
Journal:  Mod Healthc       Date:  2016-08

4.  Comparative biology: Looking for a master switch.

Authors:  Sarah Deweerdt
Journal:  Nature       Date:  2012-12-06       Impact factor: 49.962

Review 5.  Dissecting the Mechanisms Underlying Unusually Successful Human Health Span and Life Span.

Authors:  Sofiya Milman; Nir Barzilai
Journal:  Cold Spring Harb Perspect Med       Date:  2015-12-04       Impact factor: 6.915

6.  The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators.

Authors: 
Journal:  Am J Epidemiol       Date:  1989-04       Impact factor: 4.897

Review 7.  The Genetics of Aging: A Vertebrate Perspective.

Authors:  Param Priya Singh; Brittany A Demmitt; Ravi D Nath; Anne Brunet
Journal:  Cell       Date:  2019-03-21       Impact factor: 41.582

8.  Scalable and accurate deep learning with electronic health records.

Authors:  Alvin Rajkomar; Eyal Oren; Kai Chen; Andrew M Dai; Nissan Hajaj; Michaela Hardt; Peter J Liu; Xiaobing Liu; Jake Marcus; Mimi Sun; Patrik Sundberg; Hector Yee; Kun Zhang; Yi Zhang; Gerardo Flores; Gavin E Duggan; Jamie Irvine; Quoc Le; Kurt Litsch; Alexander Mossin; Justin Tansuwan; James Wexler; Jimbo Wilson; Dana Ludwig; Samuel L Volchenboum; Katherine Chou; Michael Pearson; Srinivasan Madabushi; Nigam H Shah; Atul J Butte; Michael D Howell; Claire Cui; Greg S Corrado; Jeffrey Dean
Journal:  NPJ Digit Med       Date:  2018-05-08

9.  In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state.

Authors:  Alexander Aliper; Aleksey V Belikov; Andrew Garazha; Leslie Jellen; Artem Artemov; Maria Suntsova; Alena Ivanova; Larisa Venkova; Nicolas Borisov; Anton Buzdin; Polina Mamoshina; Evgeny Putin; Andrew G Swick; Alexey Moskalev; Alex Zhavoronkov
Journal:  Aging (Albany NY)       Date:  2016-09-24       Impact factor: 5.682

10.  Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of COX7A1 in embryonic and cancer cells.

Authors:  Michael D West; Ivan Labat; Hal Sternberg; Dana Larocca; Igor Nasonkin; Karen B Chapman; Ratnesh Singh; Eugene Makarev; Alex Aliper; Andrey Kazennov; Andrey Alekseenko; Nikolai Shuvalov; Evgenia Cheskidova; Aleksandr Alekseev; Artem Artemov; Evgeny Putin; Polina Mamoshina; Nikita Pryanichnikov; Jacob Larocca; Karen Copeland; Evgeny Izumchenko; Mikhail Korzinkin; Alex Zhavoronkov
Journal:  Oncotarget       Date:  2017-12-28
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