Han Shuguang, Wang Ning, Guo Yuxin, Tang Furong, Xu Lei, Ju Ying, Shi Lei
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Beidahuang Industry Group General Hospital, Harbin, China.
Front Genet. 2021 Dec 15;12:810875. doi: 10.3389/fgene.2021.810875. eCollection 2021.
Inspired by L1-norm minimization methods, such as 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. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.
近年来，受L1范数最小化方法（如基追踪、压缩感知和套索特征选择）的启发，稀疏表示作为一种新颖且强大的数据处理方法出现，并展现出强大的优势。研究人员不仅将信号的稀疏表示扩展到图像表示，还将向量的稀疏性应用于矩阵的稀疏性。此外，稀疏表示已应用于模式识别并取得了良好的效果。由于其具有对噪声不敏感、强大的鲁棒性、对所选特征不太敏感以及无“过拟合”现象等多重优点，稀疏表示在生物信息学中的应用值得进一步研究。本文回顾了稀疏表示的发展，并阐述了其在生物信息学中的应用，即使用低秩表示矩阵来识别和研究癌症分子、低秩稀疏表示来分析和处理基因表达谱，以及介绍相关的癌症和基因表达谱数据库。
