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Front. Med. | doi: 10.3389/fmed.2019.00034

From Big Data to Precision Medicine

  • 1Philips Research (Netherlands), Netherlands
  • 2Department of Paediatrics, KK Women’s and Children’s Hospital, Singapore
  • 3Department of Medical Imaging, University of Toronto, Canada
  • 4Department of Pharmacy Practice & Science, College of Pharmacy, University of Arizona Health Sciences, United States
  • 5Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Hungary
  • 6Skejby Sygehus, Denmark
  • 7Synthetic Genomics, United States
  • 8Department of Neurology, School of Medicine, Yale University, United States
  • 9Department of Medicine, University of Cambridge, United Kingdom

For over a decade the term ‘Big data’ has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, ‘Big data’ no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as ‘data analytics’ and ‘data science’ have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalised therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises ‘Big Advances’, significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardisation of data content, format and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set ‘Big data’ analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.

Keywords: big data, precision medicine, translational medicine, data science, big data analytics

Received: 14 Jul 2018; Accepted: 04 Feb 2019.

Edited by:

Salvatore Albani, Duke-NUS Medical School, Singapore

Reviewed by:

Manuela Battaglia, San Raffaele Hospital (IRCCS), Italy
Marco Aiello, IRCCS SDN, Italy
Cornelius F. Boerkoel, National Institutes of Health (NIH), United States  

Copyright: © 2019 Hulsen, Jamuar, Moody, Karnes, Orsolya, Hedensted, Spreafico, Hafler and McKinney. 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:
Dr. Tim Hulsen, Philips Research (Netherlands), Eindhoven, Netherlands,
Dr. Eoin McKinney, Department of Medicine, University of Cambridge, Cambridge, United Kingdom,