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Mudunuri US, Khouja M, Repetski S, et al. Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PLoS One. 2013;8(12):e80503doi: 10.1371/journal.pone.0080503.
Mudunuri, U. S., Khouja, M., Repetski, S., Venkataraman, G., Che, A., Luke, B. T., Girard, F. P., & Stephens, R. M. (2013). Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PloS one, 8(12), e80503. https://doi.org/10.1371/journal.pone.0080503
Mudunuri, Uma S, et al. "Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data." PloS one vol. 8,12 (2013): e80503. doi: https://doi.org/10.1371/journal.pone.0080503
Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT, Girard FP, Stephens RM. Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PLoS One. 2013 Dec 02;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013. PMID: 24312478; PMCID: PMC3846626.
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PLoS One. 2013 Dec 02;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013.

Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data.

PloS one

Uma S Mudunuri, Mohamad Khouja, Stephen Repetski, Girish Venkataraman, Anney Che, Brian T Luke, F Pascal Girard, Robert M Stephens

Affiliations

  1. Advanced Biomedical Computing Center, Information Systems Program, SAIC-Frederick, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America.

PMID: 24312478 PMCID: PMC3846626 DOI: 10.1371/journal.pone.0080503

Abstract

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.

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