A Cloud-Based Data Network Approach for Translational Cancer Research [chapter]

Wei Xing, Dimitrios Tsoumakos, Moustafa Ghanem
2014 Advances in Experimental Medicine and Biology  
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
more » ... onsistencies. 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. Introduction Translational cancer research builds on incorporating multiple levels of biological information within clinical data with aim of gaining better understanding of how cancer works and developing new ways for identifying, preventing and treating the disease. The first challenge in conducting translational studies is that the data is heterogeneous, including phenotype, genotype, expression profiling, proteomics, protein interaction, metabolic analysis data as well as physiological measurements, etc. The second key challenge is that the data is large, decentralized, growing and continually being updated. It originates from different sources, e.g. different
doi:10.1007/978-3-319-09012-2_16 pmid:25417028 fatcat:c4otfy4q3rgs7hi4kcuwgbs5du