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网络标志物和动态网络标志物 被引量:1

Network biomarkers and dynamic network biomarkers
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摘要 网络标志物或网络生物标志物:分子浓度或表达量一般随时间和条件变化,因此传统的分子生物标志物(molecular biomarker)不能稳定地表征生物状态(如正常状态或疾病状态),而分子网络(如调控网络或相互作用网络)是相对稳定的形态,可稳定地表征生物状态。 Network biomarkers:Molecular concentration or expression generally varies with time and conditions,hence traditional molecular biomarkers cannot stably demonstrate the biological state(such as normal or diseased state),while the molecular network(such as regula?tory or interaction network)is a relatively stable form that steadily demonstrates the biological state.However,inferring biomolecular networks requires the collection of many samples by existing methods and is not suitable for clinical applica?tion.To solve the above problem,we have estab?lished a single-sample network construction theory and method.Molecular networks are established from the data onto one single simple alone,so the network biomarker[1-4]can be obtained to diag?nose disease or biological system states by networks.Network biomarkers are a group of network edges or molecular correlations,rather than a traditional group of molecular expressions.That is,the disease state or biological system state is diagnosed by changes or differences in molecular correlations.
作者 陈洛南 CHEN Luo-nan(Key Laboratory of System Biology,Shanghai Institute of Biological Sciences,Chinese Academy of Sciences;the Network Pharmacology Committee of CNPHARS)
出处 《中国药理学与毒理学杂志》 CAS CSCD 北大核心 2018年第11期835-836,共2页 Chinese Journal of Pharmacology and Toxicology
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  • 1Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N Biotechnol, 2012, 29: 613-624.
  • 2Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol, 2011, 8: 184-187.
  • 3Auffray C, Charron D, Hood L. Predictive, preventive, personalized and participatory medicine: back to the future. Genome Med, 2010, 2: 57.
  • 4Zeng T, Sun SY, Wang Y, Zhu H, Chen L. Network biomarkers reveal dysfunctional gene regulations during disease progression. FEBS J, 2013, doi:10.1111/febs.12536.
  • 5Zhong Q, Simonis N, Li QR, Charloteaux B, Heuze F, Klitgord N, Tam S, Yu H, Venkatesan K, Mou D, Swearingen V, Yildirim MA, Yan H, Dricot A, Szeto D, Lin C, Hao T, Fan C, Milstein S, Dupuy D, Brasseur R, Hill DE, Cusick ME, Vidal M. Edgetic perturbation models of human inherited disorders. Mol Syst Biol, 2009, 5: 321.
  • 6Zhang W, Zeng T, Chen L. EdgeMarker: identifying differentially correlated molecule pairs as edge-biomarkers. J Theor Biol, 2014, doi:10.1016/j.jtbi.2014.05.041.
  • 7Chen L, Liu R, Liu ZP, Li M, Aihara K. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep, 2012, 2: 342.
  • 8Liu R, Li M, Liu ZP, Wu J, Chen L, Aihara K. Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci Rep, 2012, 2: 813.
  • 9Liu R, Wang X, Aihara K, Chen L. Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers. Med Res Rev, 2013, doi:10.1002/med.21293.
  • 10Liu R, Yu X, Liu X, Xu D, Aihara K, Chen L. Identifying critical transitions of complex diseases based on a single sample. Bioinformatics, 2014, doi:10.1093/bioinformatics/btu084.

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