Single-cell classification, analysis, and its application using deep learning techniques
Single-cell analysis (SCA) improves the detection of cancer, the immune system, and chronic diseases from complicated biological processes. SCA techniques generate high-dimensional, innovative, and complex data, making traditional analysis difficult and impractical. In the different cell types, conv...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English English |
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Elsevier
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41622/ http://umpir.ump.edu.my/id/eprint/41622/1/Single-cell%20classification-%20analysis-and%20its%20application%20using%20deep%20learning%20techniques_ABST.pdf http://umpir.ump.edu.my/id/eprint/41622/2/Single-cell%20classification-analysis-and%20its%20application%20using%20deep%20learning%20techniques.pdf |
| _version_ | 1848826388076822528 |
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| author | Premkumar, R. Srinivasan, Arthi Harini Devi, K.G. Deepika, M. Gaayathry, E. Jadhav, Pramod Futane, Abhishek Narayanamurthy, Vigneswaran |
| author_facet | Premkumar, R. Srinivasan, Arthi Harini Devi, K.G. Deepika, M. Gaayathry, E. Jadhav, Pramod Futane, Abhishek Narayanamurthy, Vigneswaran |
| author_sort | Premkumar, R. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Single-cell analysis (SCA) improves the detection of cancer, the immune system, and chronic diseases from complicated biological processes. SCA techniques generate high-dimensional, innovative, and complex data, making traditional analysis difficult and impractical. In the different cell types, conventional cell sequencing methods have signal transformation and disease detection limitations. To overcome these challenges, various deep learning techniques (DL) have outperformed standard state-of-the-art computer algorithms in SCA techniques. This review discusses DL application in SCA and presents a detailed study on improving SCA data processing and analysis. Firstly, we introduced fundamental concepts and critical points of cell analysis techniques, which illustrate the application of SCA. Secondly, various effective DL strategies apply to SCA to analyze data and provide significant results from complex data sources. Finally, we explored DL as a future direction in SCA and highlighted new challenges and opportunities for the rapidly evolving field of single-cell omics. |
| first_indexed | 2025-11-15T03:44:01Z |
| format | Article |
| id | ump-41622 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:44:01Z |
| publishDate | 2024 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-416222024-06-20T03:22:27Z http://umpir.ump.edu.my/id/eprint/41622/ Single-cell classification, analysis, and its application using deep learning techniques Premkumar, R. Srinivasan, Arthi Harini Devi, K.G. Deepika, M. Gaayathry, E. Jadhav, Pramod Futane, Abhishek Narayanamurthy, Vigneswaran Q Science (General) QH Natural history TP Chemical technology Single-cell analysis (SCA) improves the detection of cancer, the immune system, and chronic diseases from complicated biological processes. SCA techniques generate high-dimensional, innovative, and complex data, making traditional analysis difficult and impractical. In the different cell types, conventional cell sequencing methods have signal transformation and disease detection limitations. To overcome these challenges, various deep learning techniques (DL) have outperformed standard state-of-the-art computer algorithms in SCA techniques. This review discusses DL application in SCA and presents a detailed study on improving SCA data processing and analysis. Firstly, we introduced fundamental concepts and critical points of cell analysis techniques, which illustrate the application of SCA. Secondly, various effective DL strategies apply to SCA to analyze data and provide significant results from complex data sources. Finally, we explored DL as a future direction in SCA and highlighted new challenges and opportunities for the rapidly evolving field of single-cell omics. Elsevier 2024-03 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41622/1/Single-cell%20classification-%20analysis-and%20its%20application%20using%20deep%20learning%20techniques_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/41622/2/Single-cell%20classification-analysis-and%20its%20application%20using%20deep%20learning%20techniques.pdf Premkumar, R. and Srinivasan, Arthi and Harini Devi, K.G. and Deepika, M. and Gaayathry, E. and Jadhav, Pramod and Futane, Abhishek and Narayanamurthy, Vigneswaran (2024) Single-cell classification, analysis, and its application using deep learning techniques. Biosystems, 237 (105142). pp. 1-12. ISSN 0303-2647. (Published) https://doi.org/10.1016/j.biosystems.2024.105142 https://doi.org/10.1016/j.biosystems.2024.105142 |
| spellingShingle | Q Science (General) QH Natural history TP Chemical technology Premkumar, R. Srinivasan, Arthi Harini Devi, K.G. Deepika, M. Gaayathry, E. Jadhav, Pramod Futane, Abhishek Narayanamurthy, Vigneswaran Single-cell classification, analysis, and its application using deep learning techniques |
| title | Single-cell classification, analysis, and its application using deep learning techniques |
| title_full | Single-cell classification, analysis, and its application using deep learning techniques |
| title_fullStr | Single-cell classification, analysis, and its application using deep learning techniques |
| title_full_unstemmed | Single-cell classification, analysis, and its application using deep learning techniques |
| title_short | Single-cell classification, analysis, and its application using deep learning techniques |
| title_sort | single-cell classification, analysis, and its application using deep learning techniques |
| topic | Q Science (General) QH Natural history TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/41622/ http://umpir.ump.edu.my/id/eprint/41622/ http://umpir.ump.edu.my/id/eprint/41622/ http://umpir.ump.edu.my/id/eprint/41622/1/Single-cell%20classification-%20analysis-and%20its%20application%20using%20deep%20learning%20techniques_ABST.pdf http://umpir.ump.edu.my/id/eprint/41622/2/Single-cell%20classification-analysis-and%20its%20application%20using%20deep%20learning%20techniques.pdf |