Recent Advances on Penalized Regression Models for Biological Data

Pei Wang (), Shunjie Chen and Sijia Yang
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Pei Wang: School of Mathematics and Statistics, 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

Mathematics, 2022, vol. 10, issue 19, 1-24

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.

Keywords: penalized regression model; RNA-seq data; sample classification; network construction; gene selection; crucial gene (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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