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Recent Advances on Penalized Regression Models for Biological Data

Author

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  • Pei Wang

    (School of Mathematics and Statistics, Henan University, Kaifeng 475004, China
    Center for Applied Mathematics of Henan Province, Henan University, Kaifeng 475004, China)

  • Shunjie Chen

    (School of Mathematics and Statistics, Henan University, Kaifeng 475004, China)

  • Sijia Yang

    (School of Mathematics and Statistics, Henan University, Kaifeng 475004, China)

Abstract

Increasingly amounts of biological data promote the development of various penalized regression models. This review discusses the recent advances in both linear and logistic regression models with penalization terms. This review is mainly focused on various penalized regression models, some of the corresponding optimization algorithms, and their applications in biological data. The pros and cons of different models in terms of response prediction, sample classification, network construction and feature selection are also reviewed. The performances of different models in a real-world RNA-seq dataset for breast cancer are explored. Finally, some future directions are discussed.

Suggested Citation

  • Pei Wang & Shunjie Chen & Sijia Yang, 2022. "Recent Advances on Penalized Regression Models for Biological Data," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3695-:d:937026
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