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Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection

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  • Sai Wang
  • Hai-Wei Shen
  • Hua Chai
  • Yong Liang

Abstract

For studying cancer and genetic diseases, the issue of identifying high correlation genes from high-dimensional data is an important problem. It is a great challenge to select relevant biomarkers from gene expression data that contains some important correlation structures, and some of the genes can be divided into different groups with a common biological function, chromosomal location or regulation. In this paper, we propose a penalized accelerated failure time model CHR-DE using a non-convex regularization (local search) with differential evolution (global search) in a wrapper-embedded memetic framework. The complex harmonic regularization (CHR) can approximate to the combination ℓ p ( 1 2 ≤ p

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  • Sai Wang & Hai-Wei Shen & Hua Chai & Yong Liang, 2019. "Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0210786
    DOI: 10.1371/journal.pone.0210786
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    References listed on IDEAS

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    3. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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