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.
Highlights
Aging is often described as the outcome of interactions among genetic, environmental and lifestyle factors with wide variation in life and health span between and within species (Newman and Murabito, 2013; Partridge et al, 2018; Singh et al, 2019)
These datasets have contributed to our understanding of human physiology and diseases but existing approaches fall short in terms of understanding the complex role of those mechanisms in aging that protect individuals from age-related diseases and enable health and life span (Sebastiani et al, 2013, 2017a; Milman and Barzilai, 2015)
As we review later, significant investments in aging and longevity research over many decades have provided a wealth of data that need to be integrated and analyzed using both statistical and Artificial intelligence (AI) approaches
Summary
Aging is often described as the outcome of interactions among genetic, environmental and lifestyle factors with wide variation in life and health span between and within species (Newman and Murabito, 2013; Partridge et al, 2018; Singh et al, 2019). Miller from the University of Michigan mentioned the availability of datasets from NIA supported non-human studies (multiple species including primates and mammals) and an animal intervention testing program (ITP) with interventions that increase mouse longevity and health span He provided examples of few compounds such as Rapamycin, 17Alpha estradiol and Acarbose that extend lifespan in mice. The datasets in Monarch can be used to integrate and make connections among other biological entities of interest, such as genes, genotypes, gene variants, models (including cell lines, animal strains, species, breeds, as well as targeted mutants) biological pathways, human orthologs and phenotypes By leveraging such big data from humans and model species to allow computers to learn, AI is expected to vastly improve recognition of patterns and relationships, allowing for broad applications in complex biological processes associated with aging. The value for aging researchers is that automated approaches take the guesswork out of which methods and parameters settings to pick making this technology more accessible to a wide audience
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