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Implications of Big Data for cell biology

Author(s): Dolinski, Kara; Troyanskaya, Olga

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dc.contributor.authorDolinski, Kara-
dc.contributor.authorTroyanskaya, Olga-
dc.date.accessioned2021-10-08T19:50:27Z-
dc.date.available2021-10-08T19:50:27Z-
dc.date.issued2015en_US
dc.identifier.citationDolinski, Kara, and Olga G. Troyanskaya. "Implications of Big Data for cell biology." Molecular Biology of the Cell 26, no. 14 (2015): pp. 2575-2578. doi: 10.1091/mbc.E13-12-0756en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr13831-
dc.description.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.en_US
dc.format.extent2575 - 2578en_US
dc.language.isoen_USen_US
dc.relation.ispartofMolecular Biology of the Cellen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleImplications of Big Data for cell biologyen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1091/mbc.E13-12-0756-
dc.identifier.eissn1939-4586-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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