Informatics and data science in cell death research
Cell death are essential for maintaining cell balance, including remove the damage or harmful cells. Disorders of cell death related to the progression of various diseases, such as cancer, and autoimmune disorders. However, some challenge about quantify, define the types, or detecting in cell death...
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| Published in: | Progress in molecular biology and translational science Vol. 217; p. 67 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Netherlands
2025
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| ISSN: | 1878-0814, 1878-0814 |
| Online Access: | Get more information |
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| Abstract | Cell death are essential for maintaining cell balance, including remove the damage or harmful cells. Disorders of cell death related to the progression of various diseases, such as cancer, and autoimmune disorders. However, some challenge about quantify, define the types, or detecting in cell death still occur. To overcome the challenges, scientists have been focusing on the applications of informatics and data science in cell death research due to the advantages and the potentials over traditional methods. The implementations of informatics and data science in cell death research have shown results in improving the efficiency of the complex data processes and modeling biological systems, thus improving the performances of diagnostic methods and procedures. The aim of this chapter is to provide an overview of the existing informatics and data science applications in cell death research. In addition, this chapter discusses the main advantages and limitations of traditional cell death research methods, with the implementation of informatics, data science, and AI to overcome the challenges. From the evidence on the topic, researchers can based on the existing findings to come up with a suitable and effective research plan, hence improving the cell death research methods in the future. |
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| AbstractList | Cell death are essential for maintaining cell balance, including remove the damage or harmful cells. Disorders of cell death related to the progression of various diseases, such as cancer, and autoimmune disorders. However, some challenge about quantify, define the types, or detecting in cell death still occur. To overcome the challenges, scientists have been focusing on the applications of informatics and data science in cell death research due to the advantages and the potentials over traditional methods. The implementations of informatics and data science in cell death research have shown results in improving the efficiency of the complex data processes and modeling biological systems, thus improving the performances of diagnostic methods and procedures. The aim of this chapter is to provide an overview of the existing informatics and data science applications in cell death research. In addition, this chapter discusses the main advantages and limitations of traditional cell death research methods, with the implementation of informatics, data science, and AI to overcome the challenges. From the evidence on the topic, researchers can based on the existing findings to come up with a suitable and effective research plan, hence improving the cell death research methods in the future. Cell death are essential for maintaining cell balance, including remove the damage or harmful cells. Disorders of cell death related to the progression of various diseases, such as cancer, and autoimmune disorders. However, some challenge about quantify, define the types, or detecting in cell death still occur. To overcome the challenges, scientists have been focusing on the applications of informatics and data science in cell death research due to the advantages and the potentials over traditional methods. The implementations of informatics and data science in cell death research have shown results in improving the efficiency of the complex data processes and modeling biological systems, thus improving the performances of diagnostic methods and procedures. The aim of this chapter is to provide an overview of the existing informatics and data science applications in cell death research. In addition, this chapter discusses the main advantages and limitations of traditional cell death research methods, with the implementation of informatics, data science, and AI to overcome the challenges. From the evidence on the topic, researchers can based on the existing findings to come up with a suitable and effective research plan, hence improving the cell death research methods in the future.Cell death are essential for maintaining cell balance, including remove the damage or harmful cells. Disorders of cell death related to the progression of various diseases, such as cancer, and autoimmune disorders. However, some challenge about quantify, define the types, or detecting in cell death still occur. To overcome the challenges, scientists have been focusing on the applications of informatics and data science in cell death research due to the advantages and the potentials over traditional methods. The implementations of informatics and data science in cell death research have shown results in improving the efficiency of the complex data processes and modeling biological systems, thus improving the performances of diagnostic methods and procedures. The aim of this chapter is to provide an overview of the existing informatics and data science applications in cell death research. In addition, this chapter discusses the main advantages and limitations of traditional cell death research methods, with the implementation of informatics, data science, and AI to overcome the challenges. From the evidence on the topic, researchers can based on the existing findings to come up with a suitable and effective research plan, hence improving the cell death research methods in the future. |
| Author | Le, Ha-Trang Vu-Thi, Hue Nguyen, Vu-Hung Quang, Huy Than Le Mau, Minh-Phuc Cao Thi, Thu-Trang Chu, Dinh-Toi |
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| Title | Informatics and data science in cell death research |
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