Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. Through a real scenario, this chapter reports on the practical use of the Dicode solution in the above context. Evaluation results show that the proposed solution enables a meaningful aggregation and analysis of large-scale data in complex biomedical research settings. Moreover, it allows for new working practices that turn the problem of information overload and cognitive complexity into the benefit of knowledge discovery.
CITATION STYLE
Tsiliki, G., & Kossida, S. (2014). Clinico-Genomic Research Assimilator: A Dicode Use Case. In Studies in Big Data (Vol. 5, pp. 165–180). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-02612-1_8
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