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

Narayanamurthy, Vigneswaran and R. Premkumar and K.G. Harini Devi and Deepika M and Gaayathry E and Srinivasan, Arthi and Jadhav, Pramod and Shankar, Futane Abhishek (2024) Single-cell classification, analysis, and its application using deep learning techniques. BioSystems, 237. 01-12. ISSN 0303-2647

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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
Uncontrolled Keywords:	Deep learning, Single-cell analysis, Data science, Single-cell classification
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	25 Jul 2024 11:26
Last Modified:	25 Jul 2024 11:26
URI:	http://eprints.utem.edu.my/id/eprint/27478
Statistic Details:	View Download Statistic

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