Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare - Rossi, Riccardo L.; Grifantini, Renata M. (Frontiers in Digital Humanities, 2018)
Research and innovation are constant imperatives for the healthcare sector: medicine, biology and biotechnology support it, and more recently computational and data-driven disciplines gained relevance to handle the massive amount of data this sector is and will be generating. To be effective in translational and healthcare industrial research, big data in the life science domain need to be organized, well annotated, catalogued, correlated and integrated: the biggest the data silos at hand, the stronger the need for organization and tidiness. The degree of such organization marks the transition from data to knowledge for strategic decision making. Thus the challenge for the use of big data in industrial research is the possibility to have effective and coherent data annotation, aimed at integration of heterogeneous domains such as different OMICs and non-OMICs (traditional) data sources. Holistic approaches enabling an acknowledged management of big data, often driven by machine learning methods, can thus trigger a change of industrial research accelerating the process from discovery to product delivery. For instance, the main pillars of industrial R&D processes for vaccines or drug development, include initial discovery, early - late pre clinics, pre-industrialization, clinical phases and finally registration - commercialization. The passage from one step to another is regulated by stringent pass/fail criteria. Bottlenecks of the R&D process are often represented by animal and human studies, which could be rationalized by surrogate in vitro assays as well as by predictive molecular and cellular signatures and models. The impact of big data in healthcare industrial research is to address such bottlenecks by providing actionable information and new knowledge so as to accelerate the development process in a cost effective way. Case studies will be discussed for the effective use of electronic health records, the leverage of network analysis methods for drug repurposing and the development of vaccines towards human pathologies.