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Predicting Patient Survival from Longitudinal Gene Expression

Author

Listed:
  • Zhang Yuping

    (Stanford University)

  • Tibshirani Robert J.

    (Stanford University)

  • Davis Ronald W.

    (Stanford University)

Abstract

Characterizing dynamic gene expression pattern and predicting patient outcome is now significant and will be of more interest in the future with large scale clinical investigation of microarrays. However, there is currently no method that has been developed for prediction of patient outcome using longitudinal gene expression, where gene expression of patients is being monitored across time. Here, we propose a novel prediction approach for patient survival time that makes use of time course structure of gene expression. This method is applied to a burn study. The genes involved in the final predictors are enriched in the inflammatory response and immune system related pathways. Moreover, our method is consistently better than prediction methods using individual time point gene expression or simply pooling gene expression from each time point.

Suggested Citation

  • Zhang Yuping & Tibshirani Robert J. & Davis Ronald W., 2010. "Predicting Patient Survival from Longitudinal Gene Expression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-23, November.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:41
    DOI: 10.2202/1544-6115.1617
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    References listed on IDEAS

    as
    1. Yuan, Ming & Kendziorski, Christina, 2006. "Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1323-1332, December.
    2. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25, January.
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    Cited by:

    1. Yuping Zhang & Zhengqing Ouyang, 2018. "Joint principal trend analysis for longitudinal high†dimensional data," Biometrics, The International Biometric Society, vol. 74(2), pages 430-438, June.

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