Venkata Satagopam Luxembourg Centre for Systems Biomedicine
Wei Gu Luxembourg Centre for Systems Biomedicine
Serge Eifes Luxembourg Centre for Systems Biomedicine
Piotr Gawron Luxembourg Centre for Systems Biomedicine
Marek Ostaszewski Luxembourg Centre for Systems Biomedicine
Stephan Gebel Luxembourg Centre for Systems Biomedicine
Adriano Barbosa-Silva Luxembourg Centre for Systems Biomedicine
Rudi Balling Luxembourg Centre for Systems Biomedicine
Reinhard Schneider Luxembourg Centre for Systems Biomedicine

As Translational Medicine Data Become More and More Rich and Complex, Their Potential in Informing both Clinical and Basic Research Grows

Translational medicine capitalizes on advances in basic life sciences to improve clinical research and care. We witness great technological advances in methods characterizing human health and disease, including genetic and environmental factors of our well-being. This is a great opportunity to understand diseases and to find new diagnoses and treatments. However, the progress comes at a cost: translational research data sets nowadays include genomic, imaging, and clinical data sources, making them large and heterogeneous. In effect, important steps of the data life cycle in discovery—integration, analysis, and interpretation—are a challenge for biomedical research. Moreover, enabling biomedical experts to efficiently use big data processing pipelines is another challenge.

As translational medicine data become more and more rich and complex, their potential in informing both clinical and basic research grows. With constantly increasing presence of high-throughput molecular profiling, it becomes increasingly important to ensure that data interpretation capabilities follow generation of large-scale biomedical data sets. Visualization can support greatly the processing of complex data sets on each of the steps of the data life cycle. This opportunity is actively explored in various domains of biomedical research, including clinical big data or multiscale biomedical ontologies.

Modern translational medicine approaches aim to combine clinical and molecular profiles of the patients to formulate informed hypothesis on the basis of stratified data. Integration of plethora of sources renders these data sets complex and difficult to process. Visualization of such integrated data sets can aid exploration and selection of key dimensions and subsets for downstream analysis. In turn, visually aided data analysis allows to comprehend even complicated workflows and aids interpretation of resulting data.

In this article, we demonstrate a workflow for translational medicine big data, in which visualization is an important component at each step of data processing and exploration. We describe in detail the interfaces allowing the construction of the workflow, followed up by a use case scenario. We conclude with a discussion of the results and an outlook for future development of visualization in biomedical big data exploration.

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Big Data, published by Mary Ann Liebert, Inc., a highly innovative, peer-reviewed journal, provides a unique forum for world-class research exploring the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data, including data science, big data infrastructure and analytics, and pervasive computing. The above article was first published in the June 2016 issue of Big Data with the title “Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases”. The views expressed here are those of the authors and are not necessarily those of Big Data, Mary Ann Liebert, Inc., publishers, or their affiliates. No endorsement of any entity or technology is implied.

 

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