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Survival prediction using gene expression data: A review and comparison

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  • van Wieringen, Wessel N.
  • Kun, David
  • Hampel, Regina
  • Boulesteix, Anne-Laure

Abstract

Knowledge of transcription of the human genome might greatly enhance our understanding of cancer. In particular, gene expression may be used to predict the survival of cancer patients. Microarray data are characterized by their high-dimensionality: the number of covariates (p~1000) greatly exceeds the number of samples (n~100), which is a considerable challenge in the context of survival prediction. An inventory of methods that have been used to model survival using gene expression is given. These methods are critically reviewed and compared in a qualitative way. Next, these methods are applied to three real-life data sets for a quantitative comparison. The choice of the evaluation measure of predictive performance is crucial for the selection of the best method. Depending on the evaluation measure, either the L2-penalized Cox regression or the random forest ensemble method yields the best survival time prediction using the considered gene expression data sets. Consensus on the best evaluation measure of predictive performance is needed.

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  • van Wieringen, Wessel N. & Kun, David & Hampel, Regina & Boulesteix, Anne-Laure, 2009. "Survival prediction using gene expression data: A review and comparison," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1590-1603, March.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1590-1603
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    2. Yu Takagi & Hirokazu Matsuda & Yukio Taniguchi & Hiroaki Iwaisaki, 2014. "Predicting the Phenotypic Values of Physiological Traits Using SNP Genotype and Gene Expression Data in Mice," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-17, December.
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    4. Zhao, Xiaobing & Zhou, Xian, 2014. "Sufficient dimension reduction on marginal regression for gaps of recurrent events," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 56-71.
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    7. Emura, Takeshi & Chen, Yi-Hau & Chen, Hsuan-Yu, 2012. "Survival prediction based on compound covariate under cox proportional hazard models," MPRA Paper 41149, University Library of Munich, Germany.
    8. Yanfeng Wang & Haohao Wang & Sanyi Li & Lidong Wang, 2022. "Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 10(9), pages 1-20, April.
    9. Anestis Antoniadis & Piotr Fryzlewicz & Frédérique Letué, 2010. "The Dantzig Selector in Cox's Proportional Hazards Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 531-552, December.
    10. Luke Kumar & Russell Greiner, 2019. "Gene expression based survival prediction for cancer patients—A topic modeling approach," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-30, November.
    11. Wei Zhang & Takayo Ota & Viji Shridhar & Jeremy Chien & Baolin Wu & Rui Kuang, 2013. "Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-16, March.
    12. Armin Rauschenberger & Iuliana Ciocănea-Teodorescu & Marianne A. Jonker & Renée X. Menezes & Mark A. Wiel, 2020. "Sparse classification with paired covariates," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 571-588, September.
    13. Ming Yi & Ruoqing Zhu & Robert M Stephens, 2018. "GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-28, December.
    14. Farcomeni, Alessio & Nardi, Alessandra, 2010. "A two-component Weibull mixture to model early and late mortality in a Bayesian framework," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 416-428, February.
    15. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    16. Julia Gilhodes & Florence Dalenc & Jocelyn Gal & Christophe Zemmour & Eve Leconte & Jean Marie Boher & Thomas Filleron, 2020. "Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings," Post-Print hal-02934793, HAL.
    17. Xiaolin Chen & Catherine Chunling Liu & Sheng Xu, 2021. "An efficient algorithm for joint feature screening in ultrahigh-dimensional Cox’s model," Computational Statistics, Springer, vol. 36(2), pages 885-910, June.

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