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Sparse pathway-based prediction models for high-throughput molecular data

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  • Lee, Sangin
  • Lee, Youngjo
  • Pawitan, Yudi

Abstract

Pathway-based prediction problems for high-throughput molecular data motivate the development of sparsity-constrained models with structured predictive variables. Intuitively it is desirable to incorporate the structural information into the model building procedure, potentially for improving both interpretability and prediction performances. Various random-effect models are developed for structured sparse prediction where the predictive variables/genes can be naturally grouped into overlapping groups or pathways. The hierarchical likelihood approach can be used for these random-effect models that impose sparse selection of the overlapping groups as well as further selection within the selected groups. In addition, the approach leads to a unified optimization algorithm for these random-effect models. Extensive numerical studies based on simulated and real breast-cancer data demonstrate that the proposed methods perform well against existing methods that ignore the structural information.

Suggested Citation

  • Lee, Sangin & Lee, Youngjo & Pawitan, Yudi, 2018. "Sparse pathway-based prediction models for high-throughput molecular data," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 125-135.
    • Handle: RePEc:eee:csdana:v:126:y:2018:i:c:p:125-135
    DOI: 10.1016/j.csda.2018.04.012
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    References listed on IDEAS

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