Recent Advances on Penalized Regression Models for Biological Data

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.

Automated summary

This review focuses on recent advances in penalized regression models, including linear and logistic regression models, 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 discussed. The performance of different models in a real-world RNA-seq dataset for breast cancer is explored, and future directions are suggested.