首页|University of Warwick Reports Findings in Machine Learning (Multiscale characterization and analysis of cellular viscoelastic mechanical phenotypes by atomic force microscopy)
University of Warwick Reports Findings in Machine Learning (Multiscale characterization and analysis of cellular viscoelastic mechanical phenotypes by atomic force microscopy)
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Wiley
New research on Machine Learning is the subject of a report. According to news reporting originating in Coventry, United Kingdom, by NewsRx journalists, research stated, “The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells.” The news reporters obtained a quote from the research from the University of Warwick, “To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 mm/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification. RESEARCH HIGHLIGHTS: This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells.”
NETL
NSTL
Wiley
