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Helzlsouer K, Meerzaman D, Taplin S, et al. Humanizing Big Data: Recognizing the Human Aspect of Big Data. Front Oncol. 2020;10:186doi: 10.3389/fonc.2020.00186.
Helzlsouer, K., Meerzaman, D., Taplin, S., & Dunn, B. K. (2020). Humanizing Big Data: Recognizing the Human Aspect of Big Data. Frontiers in oncology, 10186. https://doi.org/10.3389/fonc.2020.00186
Helzlsouer, Kathy, et al. "Humanizing Big Data: Recognizing the Human Aspect of Big Data." Frontiers in oncology vol. 10 (2020): 186. doi: https://doi.org/10.3389/fonc.2020.00186
Helzlsouer K, Meerzaman D, Taplin S, Dunn BK. Humanizing Big Data: Recognizing the Human Aspect of Big Data. Front Oncol. 2020 Mar 13;10:186. doi: 10.3389/fonc.2020.00186. eCollection 2020. PMID: 32231993; PMCID: PMC7082327.
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Front Oncol. 2020 Mar 13;10:186. doi: 10.3389/fonc.2020.00186. eCollection 2020.

Humanizing Big Data: Recognizing the Human Aspect of Big Data.

Frontiers in oncology

Kathy Helzlsouer, Daoud Meerzaman, Stephen Taplin, Barbara K Dunn

Affiliations

  1. Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, United States.
  2. Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States.
  3. Center for Global Health, National Cancer Institute, Bethesda, MD, United States.
  4. Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, United States.

PMID: 32231993 PMCID: PMC7082327 DOI: 10.3389/fonc.2020.00186

Abstract

The term "big data" refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as "recreational" genetic testing such as ancestry testing, as well as research studies, provide one type of "big data." Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this "humanizing" process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.

Copyright © 2020 Helzlsouer, Meerzaman, Taplin and Dunn.

Keywords: big data; cancer risk prediction; clinical genetics/genomics; data sharing; direct-to-consumer testing; precision medicine; predictive analytics

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