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Mol Biol Cell. 2015 Jul 15;26(14):2575-8. doi: 10.1091/mbc.E13-12-0756.

Implications of Big Data for cell biology.

Author information

1
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540 dolinski@princeton.edu ogt@genomics.princeton.edu.
2
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540 Department of Computer Science, Princeton University, Princeton, NJ 08540 Simons Center for Data Analysis, Simons Foundation, New York, NY 10010 dolinski@princeton.edu ogt@genomics.princeton.edu.

Abstract

"Big Data" has surpassed "systems biology" and "omics" as the hottest buzzword in the biological sciences, but is there any substance behind the hype? Certainly, we have learned about various aspects of cell and molecular biology from the many individual high-throughput data sets that have been published in the past 15-20 years. These data, although useful as individual data sets, can provide much more knowledge when interrogated with Big Data approaches, such as applying integrative methods that leverage the heterogeneous data compendia in their entirety. Here we discuss the benefits and challenges of such Big Data approaches in biology and how cell and molecular biologists can best take advantage of them.

PMID:
26174066
PMCID:
PMC4501356
DOI:
10.1091/mbc.E13-12-0756
[Indexed for MEDLINE]
Free PMC Article

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