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Background
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one

Background
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.
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    Date Created
    • 2011-12-21
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  • Text
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    Identifier
    • Digital object identifier: 10.1186/1745-6150-6-64
    • Identifier Type
      International standard serial number
      Identifier Value
      0956-5663
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    Kostelich, E. J., Kuang, Y., Mcdaniel, J. M., Moore, N. Z., Martirosyan, N. L., & Preul, M. C. (2011). Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors. Biology Direct, 6(1), 64. doi:10.1186/1745-6150-6-64

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