From Big Data to Precision Medicine

By Tim Hulsen, Saumya Shekhar Jamuar, Alan R. Moody, Jason Karnes, Orsolya Gyongyi Varga, Stine Hedensted, Roberto Spreafico, David A. Hafler, Eoin F. McKinney. Frontiers in Medicine, 2019

400 citations recorded by the source corpus.

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 personalized 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 standardization 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.

Subfields: Artificial Intelligence, Health Information Management, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/ecef432e7f6c9f431d5b34706a8de1fdebec46f9

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Alfredo Vellido

Citations in the corpus

Utilizing graph machine learning within drug discovery and development (2021, methodology): “com experimental and clinical trial data collection [8] including the use of devices [9, 10] to precision medicine and the use of ‘big data’ [11].”
Deep Learning for Health Informatics (2017, background): “this big data would provide valuable insight into disease management [138], [139].”
Dataset Featurization: Uncovering Natural Language Features through Unsupervised Data Reconstruction (2025, unknown): “Extracting meaningful insights from large data repositories is a cornerstone of modern research, spanning disciplines such as the social sciences (Lazer et al., 2009; Xu et al., 2024), medical sciences (Hulsen et al., 2019), and economics (Varian, 2014; Korinek, 2023).”