Description

The idea that the purely phenomenological knowl-edge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH re-searchers to build detailed mechanistic models for individual pa-tients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technolo-gies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further devel-oped to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, includ-ing nontextual information; distributed data management under security and performance constraints; specialized analytics to inte-grate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the “physiological envelope” during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.