Application of Sparse Representation in Bioinformatics

Han, Shuguang and Wang, Ning and Guo, Yuxin and Tang, Furong and Xu, Lei and Ju, Ying and Shi, Lei (2021) Application of Sparse Representation in Bioinformatics. Frontiers in Genetics, 12. ISSN 1664-8021

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Abstract

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.

Item Type: Article
Subjects: GO STM Archive > Medical Science
Depositing User: Unnamed user with email support@gostmarchive.com
Date Deposited: 07 Jan 2023 10:11
Last Modified: 02 Jun 2024 13:42
URI: http://journal.openarchivescholar.com/id/eprint/19

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