Feature selection based on functional group structure for microRNA expression data analysis

Feature selection methods have been widely used in gene expression analysis to identify differentially expressed genes and explore potential biomarkers for complex diseases. While a lot of studies have shown that incorporating feature structure information can greatly enhance the performance of feature selection algorithms, and genes naturally fall into groups with regard to common function and co-regulation, only a few of gene expression studies utilized the structured properties. And, as far as we know, there has been no such study on microRNA (miRNA) expression analysis due to the lack of available functional annotation for miRNAs. In this study, we focus on miRNA expression analysis because of its importance in the diagnosis, prognosis prediction and new therapeutic target detection for complex diseases. MiRNAs tend to work in groups to play their regulation roles, thus the miRNA expression data also has group structure. We utilize the GO-based semantic similarity to infer miRNA functional groups, and propose a new feature selection method taking group structure into consideration, called MiRFFS (MiRNA Functional group-based Feature Selection). We also apply the group information to the sparse group Lasso method, and compare MiRFFS with the sparse group Lasso as well as some existing feature selection methods. The results on three miRNA microarray profiles of breast cancer show that MiRFFS can achieve a compact feature subset with high classification accuracy.