Single-cell classification, analysis, and its application using deep learning techniques

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)

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Abstract

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.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Data science; Deep learning; Single-cell analysis; Single-cell classification
Subjects:	Q Science > Q Science (General) Q Science > QH Natural history T Technology > TP Chemical technology
Faculty/Division:	Faculty of Industrial Sciences And Technology Institute of Postgraduate Studies Faculty of Chemical and Process Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	20 Jun 2024 03:22
Last Modified:	20 Jun 2024 03:22
URI:	http://umpir.ump.edu.my/id/eprint/41622
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