Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy

doi:10.1016/j.csbj.2022.12.050

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	Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy
	K. Liu; B. Liu; Y. Zhang; Q. Wu; M. Zhong; L. Shang; Y. Wang; P. Liang; W. Wang; Q. Zhao and B. Li
	2023
发表期刊	Computational and Structural Biotechnology Journal
ISSN	20010370
卷号	21 页码:802-811
摘要	Cell misuse and cross-contamination can affect the accuracy of cell research results and result in wasted time, manpower and material resources. Thus, cell line identification is important and necessary. At present, the commonly used cell line identification methods need cell staining and culturing. There is therefore a need to develop a new method for the rapid and automated identification of cell lines. Raman spectroscopy has become one of the emerging techniques in the field of microbial identification, with the advantages of being rapid and noninvasive and providing molecular information for biological samples, which is beneficial in the identification of cell lines. In this study, we built a library of Raman spectra for gastric mucosal epithelial cell lines GES-1 and gastric cancer cell lines, such as AGS, BGC-823, HGC-27, MKN-45, MKN-74 and SNU-16. Five spectral datasets were constructed using spectral data and included the full spectrum, fingerprint region, high-wavelength number region and Raman background of Raman spectra. A stacking ensemble learning model, SL-Raman, was built for different datasets, and gastric cancer cell identification was achieved. For the gastric cancer cells we studied, the differentiation accuracy of SL-Raman was 100% for one of the gastric cancer cells and 100% for six of the gastric cancer cells. Additionally, the separation accuracy for two gastric cancer cells with different degrees of differentiation was 100%. These results demonstrate that Raman spectroscopy combined with SL-Raman may be a new method for the rapid and accurate identification of gastric cancer. In addition, the accuracy of 94.38% for classifying Raman spectral background data using machine learning demonstrates that the Raman spectral background contains some useful spectral features. These data have been overlooked in previous studies. © 2023 The Authors
DOI	10.1016/j.csbj.2022.12.050
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收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67697
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	K. Liu,B. Liu,Y. Zhang,et al. Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy[J]. Computational and Structural Biotechnology Journal,2023,21:802-811.
APA	K. Liu.,B. Liu.,Y. Zhang.,Q. Wu.,M. Zhong.,...&Q. Zhao and B. Li.(2023).Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy.Computational and Structural Biotechnology Journal,21,802-811.
MLA	K. Liu,et al."Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy".Computational and Structural Biotechnology Journal 21(2023):802-811.

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