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

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.

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

Figure 1

Maximum Life Span across different species [adapted from Deweerdt (2012), Copyright received from Nature Journal].

Figure 2

Extensive phenotypic characterization of the cohorts. CMS indicates Centers for Medicare and Medicaid Services data [adapted from Benjamin et al. (2015). Copyright received from Circulation Journal].

Figure 3

Relationship of AI, ML, and DL (Modified from Toward Data Science).