High-Performance Computing (HPC) is a mature domain that proved to be critical in running largescale modelling and simulation using numerical models. The Big Data Analytics domain (BDA) hasbeen rapidly developed over the last decade to process vast amounts of data now being generated invarious fields. Data-intensive applications are needed in various fields of medicine and healthcareranges from advanced research, as genomics, proteomics, epidemiology, and systems biology, tomedical diagnosis and treatments, or to commercial initiatives to develop new drugs. BDA needs theinfrastructure and the fundamentals of HPC to face with the required computational challenges.There are important differences in the approaches of these two domains, those that are working inBDA focus on big data specific features such as Volume, Velocity, Variety, Veracity, and Value, whileHPC scientists tend to focus on Performance, Scaling, and Power efficiency of computations. In thispaper we intend to analyse the need of these two domains in the development of the future medicinethat is required to become at least Personalized, Predictive, Preventive, and Participatory.
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