Although sometimes overhyped, big data technologies do have great potential in the domain of computational biomedicine, but their development should take place in combination with other modeling strategies, and not in competition.This will minimize the risk of research investments, and will ensure a constant improvement of in silico medicine, favoring
its clinical adoption. We have described five major problems that we believe need to be tackled in order to have an effective integration of big data analytics and VPH modeling in healthcare. For some of these problems there is already an intense on-going research activity,
which is comforting. For many years the high-performance computing world was afflicted by a one-size-fits-all mentality that prevented many research domains from fully exploiting the potential of these technologies; more recently the promotion of centres of excellence, etc., targeting specific application domains, demonstrates that the original strategy was a mistake, and that technological research must be conducted at least in part in the context of each application domain. It is very important that the big data research community does not repeat the same mistake. While there is clearly an important research space examining the fundamental methods and technologies for big data analytics, it is vital to acknowledge that it is also necessary to fund domain-targeted research that allows specialized solutions to be developed for specific applications. Healthcare, in general, and computational biomedicine,
in particular, seems a natural candidate for this.