Application of Sparse Representation in Bioinformatics - Han, Shuguang; Wang, Ning; Guo, Yuxin; Tang, Furong; Xu, Lei; Ju, Ying; Shi, Lei (Frontiers in Genetics, 2021)
Inspired by L1-norm minimization methods, including basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no "overfitting" phenomenon, the application of sparse representation in bioinformatics should be studied further. In this article, the development of sparse representation is reviewed and its applications in bioinformatics are presented, that is, the use of low-rank representation matrices to identify and study cancer molecules, and the use of low-rank sparse representation to analyze and process gene expression profiles, and related cancers and a gene expression profile database.