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Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics

Author

Listed:
  • Sinisi Sandra E

    (University of California, Berkeley)

  • van der Laan Mark J.

    (University of California, Berkeley)

Abstract

van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment where a parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using a loss function. After briefly reviewing this approach, this article proposes a general deletion/substitution/addition algorithm for minimizing, over subsets of variables (e.g., basis functions), the empirical risk of subset-specific estimators of the parameter of interest. This algorithm provides us with a new class of loss-based cross-validated algorithms in prediction of univariate outcomes, which can be extended to handle multivariate outcomes, conditional density and hazard estimation, and censored outcomes such as survival. In the context of regression, using polynomial basis functions, we study the properties of the deletion/substitution/addition algorithm in simulations and apply the method to detect transcription factor binding sites in yeast gene expression experiments.

Suggested Citation

  • Sinisi Sandra E & van der Laan Mark J., 2004. "Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-40, August.
    • Handle: RePEc:bpj:sagmbi:v:3:y:2004:i:1:n:18
    DOI: 10.2202/1544-6115.1069
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    Citations

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    Cited by:

    1. Wang, Hui & Rose, Sherri & van der Laan, Mark J., 2011. "Finding quantitative trait loci genes with collaborative targeted maximum likelihood learning," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 792-796, July.
    2. Sinisi Sandra E. & Neugebauer Romain & van der Laan Mark J., 2006. "Cross-Validated Bagged Prediction of Survival," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-26, May.
    3. Neugebauer Romain & Schmittdiel Julie A. & van der Laan Mark J., 2016. "A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 131-155, May.
    4. Odden Michelle C. & Tager Ira B. & van der Laan Mark J. & Delaney Joseph A.C. & Peralta Carmen A & Katz Ronit & Sarnak Mark J. & Psaty Bruce M. & Shlipak Michael G, 2011. "Antihypertensive Medication Use and Change in Kidney Function in Elderly Adults: A Marginal Structural Model Analysis," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-19, September.
    5. Elise D Riley & Torsten B Neilands & Kelly Moore & Jennifer Cohen & David R Bangsberg & Diane Havlir, 2012. "Social, Structural and Behavioral Determinants of Overall Health Status in a Cohort of Homeless and Unstably Housed HIV-Infected Men," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    6. Petersen, Maya L. & Molinaro, Annette M. & Sinisi, Sandra E. & van der Laan, Mark J., 2007. "Cross-validated bagged learning," Journal of Multivariate Analysis, Elsevier, vol. 98(9), pages 1693-1704, October.

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