Abstract
We develop a new model and associated technology for constructing and managing self-organizing data to support translational cancer research studies. We employ a semantic content network approach to address the challenges of managing cancer research data. Such data is heterogeneous, large, decentralized, growing and continually being updated. Moreover, the data originates from different information sources that may be partially overlapping, creating redundancies as well as contradictions and inconsistencies. Building on the advantages of elasticity of cloud computing, we deploy the cancer data networks on top of the CELAR Cloud platform to enable more effective processing and analysis of Big cancer data.
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This work is supported by EU FP7 CELAR project.
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Xing, W., Tsoumakos, D., Ghanem, M. (2015). A Cloud-Based Data Network Approach for Translational Cancer Research. In: Vlamos, P., Alexiou, A. (eds) GeNeDis 2014. Advances in Experimental Medicine and Biology, vol 820. Springer, Cham. https://doi.org/10.1007/978-3-319-09012-2_16
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DOI: https://doi.org/10.1007/978-3-319-09012-2_16
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