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

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

SUBMITTER: Mudunuri US 

PROVIDER: S-EPMC3846626 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature