Meta-Analysis Based on Nonconvex Regularization.

ABSTRACT: The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers in different studies. Meta-analysis is an effective approach to deal with these problems, but the current methods have some limitations. In this paper, we propose the meta-analysis based on three nonconvex regularization methods, which are L_1/2 regularization (meta-Half), Minimax Concave Penalty regularization (meta-MCP) and Smoothly Clippe

SUBMITTER: Zhang H 

PROVIDER: S-EPMC7113298 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature