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Big Data. 2016 Jun;4(2):97-108. doi: 10.1089/big.2015.0057.

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases.

Author information

1
1 Luxembourg Centre for Systems Biomedicine, University of Luxembourg , Esch-Belval, Luxembourg .
2
2 Information Technology for Translational Medicine (ITTM) S.A. , Esch-Belval, Luxembourg .

Abstract

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

KEYWORDS:

big data analytics; big data infrastructure design; data acquisition and cleaning; data integration; data mining; disease map

PMID:
27441714
PMCID:
PMC4932659
DOI:
10.1089/big.2015.0057
[Indexed for MEDLINE]
Free PMC Article

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