Paper Info
Title
Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases.
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 servicestranSMART, a Galaxy Server, and a MINERVA platformare 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.
Year
DOI
Venue
2016
10.1089/big.2015.0057
BIG DATA
Keywords
DocType
Volume
big data analytics,big data infrastructure design,data acquisition and cleaning,data integration,data mining,disease map
Journal
4
Issue
ISSN
Citations 
SP2
2167-6461
3
PageRank 
References 
Authors
0.40
27
9
Name
Order
Citations
PageRank
Venkata P Satagopam111610.76
Wei Gu292.38
Serge Eifes330.40
Piotr Gawron44711.23
Marek Ostaszewski5297.04
Stephan Gebel650.81
Adriano Barbosa-Silva71558.15
Rudi Balling851.14
Reinhard Schneider947528.53