• DocumentCode
    555506
  • Title

    The early warning signal of complex diseases based on the network transition entropy

  • Author

    Liu, Rui ; Chen, Luonan ; Aihara, Kazuyuki

  • Author_Institution
    Collaborative Res. Center for Innovative Math. Modelling, Univ. of Tokyo, Tokyo, Japan
  • fYear
    2011
  • fDate
    2-4 Sept. 2011
  • Firstpage
    362
  • Lastpage
    367
  • Abstract
    Many evidences suggested that during the progression of complex diseases, the deteriorations are generally not smooth but abrupt, which may cause a critical transition from one state to another at a tipping point, corresponding to a bifurcation of the dynamical system for the underlying organism. A pre-disease state is assumed to exist before reaching the tipping point between a normal state and a disease state. Since the predisease state is defined as a limit of the normal state, which represents an early-warning signal of the disease, it is crucial to identify such a state so that remedial actions can be executed to avoid the abrupt transition to the disease state. Although most complex diseases are model free, and usually only small samples are available due to clinical limitations, we propose that an index called the network transition entropy (NTE) may serving as an early-warning indicator for predicting the critical transition. Although the theoretical deviation is based on the dynamical network biomarker (DNB), the application of NTE is DNB free.
  • Keywords
    bifurcation; biochemistry; complex networks; diseases; entropy; nonlinear dynamical systems; DNB; NTE; complex disease early warning signal; complex disease progression; critical transition; dynamical network biomarker; dynamical system bifurcation; network transition entropy; predisease state; tipping point; Conferences; Correlation; Diseases; Entropy; Indexes; Proteins; Systems biology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems Biology (ISB), 2011 IEEE International Conference on
  • Conference_Location
    Zhuhai
  • Print_ISBN
    978-1-4577-1661-4
  • Electronic_ISBN
    978-1-4577-1665-2
  • Type

    conf

  • DOI
    10.1109/ISB.2011.6033179
  • Filename
    6033179