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A Novel Meta-Analysis-Based Regularized Orthogonal Matching Pursuit Algorithm to Predict Lung Cancer with Selected Biomarkers

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  • Sai Wang

    (College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China
    College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China)

  • Bin-Yuan Wang

    (College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China)

  • Hai-Fang Li

    (College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China
    College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

Biomarker selection for predictive analytics encounters the problem of identifying a minimal-size subset of genes that is maximally predictive of an outcome of interest. For lung cancer gene expression datasets, it is a great challenge to handle the characteristics of small sample size, high dimensionality, high noise as well as the low reproducibility of important biomarkers in different studies. In this paper, our proposed meta-analysis-based regularized orthogonal matching pursuit (MA-ROMP) algorithm not only gains strength by using multiple datasets to identify important genomic biomarkers efficiently, but also keeps the selection flexible among datasets to take into account data heterogeneity through a hierarchical decomposition on regression coefficients. For a case study of lung cancer, we downloaded GSE10072, GSE19188 and GSE19804 from the GEO database with inconsistent experimental conditions, sample preparation methods, different study groups, etc. Compared with state-of-the-art methods, our method shows the highest accuracy, of up to 95.63%, with the best discriminative ability (AUC 0.9756) as well as a more than 15-fold decrease in its training time. The experimental results on both simulated data and several lung cancer gene expression datasets demonstrate that MA-ROMP is a more effective tool for biomarker selection and learning cancer prediction.

Suggested Citation

  • Sai Wang & Bin-Yuan Wang & Hai-Fang Li, 2023. "A Novel Meta-Analysis-Based Regularized Orthogonal Matching Pursuit Algorithm to Predict Lung Cancer with Selected Biomarkers," Mathematics, MDPI, vol. 11(19), pages 1-13, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4171-:d:1253880
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    References listed on IDEAS

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    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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