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BioMed Research International
Volume 2014 (2014), Article ID 134023, 13 pages
Review Article

Managing, Analysing, and Integrating Big Data in Medical Bioinformatics: Open Problems and Future Perspectives

1Bioinformatics Research Unit, Institute for Biomedical Technologies, National Research Council of Italy, Segrate, 20090 Milan, Italy
2Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Science Department, Universidad Católica San Antonio de Murcia (UCAM), 30107 Murcia, Spain
3Department of Computer Science and Engineering, Center for Research Computing, University of Notre Dame, P.O. Box 539, Notre Dame, IN 46556, USA
4Advanced Computing Systems and High Performance Computing Group, Institute of Applied Mathematics and Information Technologies, National Research Council of Italy, 16149 Genoa, Italy

Received 18 June 2014; Accepted 13 August 2014; Published 1 September 2014

Academic Editor: Carlo Cattani

Copyright © 2014 Ivan Merelli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The explosion of the data both in the biomedical research and in the healthcare systems demands urgent solutions. In particular, the research in omics sciences is moving from a hypothesis-driven to a data-driven approach. Healthcare is additionally always asking for a tighter integration with biomedical data in order to promote personalized medicine and to provide better treatments. Efficient analysis and interpretation of Big Data opens new avenues to explore molecular biology, new questions to ask about physiological and pathological states, and new ways to answer these open issues. Such analyses lead to better understanding of diseases and development of better and personalized diagnostics and therapeutics. However, such progresses are directly related to the availability of new solutions to deal with this huge amount of information. New paradigms are needed to store and access data, for its annotation and integration and finally for inferring knowledge and making it available to researchers. Bioinformatics can be viewed as the “glue” for all these processes. A clear awareness of present high performance computing (HPC) solutions in bioinformatics, Big Data analysis paradigms for computational biology, and the issues that are still open in the biomedical and healthcare fields represent the starting point to win this challenge.