SPLSN: An efficient tool for survival analysis and biomarker selection. - Liang Yong

In genome research, it is a fundamental issue to identify few but important survival‐related biomarkers. The Cox model is a widely used survival analysis technique, which is used to study the relationship between characteristics and survival response. However, limitations of the existing Cox methods for genomic data are as follows: (1) a typical gene expression data set consists of tens of thousands of genes, and the result of current methods may not be sparse enough; (2) a wealth of structural information about many biological processes, such as regulatory networks and pathways, has often been ignored; (3) genomic data is usually considered as high noise, which is usually ignored in current methods. To alleviate the above problems, in this paper, we study a novel sparse Cox regression model, called SPLSN, which combines self‐paced learning (SPL) and a log‐sum absolute network‐based penalty (Logsum …