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Drug Discov Today. 2019 Sep 6. pii: S1359-6446(19)30337-X. doi: 10.1016/j.drudis.2019.08.008. [Epub ahead of print]

Moving beyond the current limits of data analysis in longevity and healthy lifespan studies.

Author information

1
Bio-Data Science and Education Research Group, School of Biological Sciences, Nanyang Technological University, 637551, Singapore. Electronic address: wilsongoh@ntu.edu.sg.
2
Lipid Regulation and Cell Stress Research Group, School of Biological Sciences, Nanyang Technological University, 637551, Singapore.
3
Lipid Regulation and Cell Stress Research Group, School of Biological Sciences, Nanyang Technological University, 637551, Singapore; Institute of Molecular and Cell Biology, A⁎STAR, 138673, Singapore. Electronic address: thibault@ntu.edu.sg.

Abstract

Living longer with sustainable quality of life is becoming increasingly important in aging populations. Understanding associative biological mechanisms have proven daunting, because of multigenicity and population heterogeneity. Although Big Data and Artificial Intelligence (AI) could help, naïve adoption is ill advised. We hold the view that model organisms are better suited for big-data analytics but might lack relevance because they do not immediately reflect the human condition. Resolving this hurdle and bridging the human-model organism gap will require some finesse. This includes improving signal:noise ratios by appropriate contextualization of high-throughput data, establishing consistency across multiple high-throughput platforms, and adopting supporting technologies that provide useful in silico and in vivo validation strategies.

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