Application of Sparse Representation in Bioinformatics
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 representati...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.810875/full |
| _version_ | 1819101185506803712 |
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| author | Shuguang Han Ning Wang Yuxin Guo Yuxin Guo Furong Tang Furong Tang Lei Xu Ying Ju Lei Shi |
| author_facet | Shuguang Han Ning Wang Yuxin Guo Yuxin Guo Furong Tang Furong Tang Lei Xu Ying Ju Lei Shi |
| author_sort | Shuguang Han |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T01:14:39Z |
| format | Article |
| id | doaj.art-8b55e25542874623a47364a92891901e |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T01:14:39Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-8b55e25542874623a47364a92891901e2022-12-21T18:43:54ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-12-011210.3389/fgene.2021.810875810875Application of Sparse Representation in BioinformaticsShuguang Han0Ning Wang1Yuxin Guo2Yuxin Guo3Furong Tang4Furong Tang5Lei Xu6Ying Ju7Lei Shi8Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, ChinaBeidahuang Industry Group General Hospital, Harbin, ChinaYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, ChinaSchool of Mathematics and Statistics, Hainan Normal University, Haikou, ChinaYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, ChinaSchool of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, ChinaSchool of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, ChinaSchool of Informatics, Xiamen University, Xiamen, ChinaDepartment of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai, ChinaInspired 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.https://www.frontiersin.org/articles/10.3389/fgene.2021.810875/fullsparse representationgene expression profilemachine learninglow-rank representationcancer |
| spellingShingle | Shuguang Han Ning Wang Yuxin Guo Yuxin Guo Furong Tang Furong Tang Lei Xu Ying Ju Lei Shi Application of Sparse Representation in Bioinformatics Frontiers in Genetics sparse representation gene expression profile machine learning low-rank representation cancer |
| title | Application of Sparse Representation in Bioinformatics |
| title_full | Application of Sparse Representation in Bioinformatics |
| title_fullStr | Application of Sparse Representation in Bioinformatics |
| title_full_unstemmed | Application of Sparse Representation in Bioinformatics |
| title_short | Application of Sparse Representation in Bioinformatics |
| title_sort | application of sparse representation in bioinformatics |
| topic | sparse representation gene expression profile machine learning low-rank representation cancer |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.810875/full |
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