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Insights into cell classification based on combination of multiple cellular mechanical phenotypes by using machine

Yanling Tian1, Wangjiang Lin2, Kaige Qu3

  • 1Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.

Journal of the Mechanical Behavior of Biomedical Materials
|February 12, 2022
PubMed
Summary
This summary is machine-generated.

Cellular elastic property alone is insufficient for cancer detection. Machine learning algorithms analyzing multiple cellular mechanical phenotypes offer a more effective approach for classifying cancerous cells and aiding cancer prognostics.

Keywords:
AdhesivenessCellular classificationCellular elastic property (CEP)Cellular mechanical phenotype (CMP)Cellular membrane tension (CMT)Work of adhesion (WoA)

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Area of Science:

  • Biophysics
  • Cell Biology
  • Computational Biology

Background:

  • Cellular elastic property (CEP) is often used as a biomarker to differentiate cancerous from benign cells.
  • However, CEP alone is not a universal indicator for all cell types, as demonstrated in gastric cancer.
  • Insignificant statistical differences in CEP were observed between normal and cancerous gastric cells.

Purpose of the Study:

  • To propose and evaluate multiple cellular mechanical phenotypes (CMPs) for differentiating normal and cancerous gastric cells.
  • To assess the efficacy of machine learning algorithms (MLAs) in classifying cell types based on CMPs.
  • To determine if incorporating more CMPs improves classification accuracy.

Main Methods:

  • Utilized machine learning algorithms (MLAs) to analyze cellular mechanical phenotypes (CMPs).
  • Compared classification performance using varying numbers of CMPs.
  • Validated the MLA-based method with additional cell lines.

Main Results:

  • Machine learning algorithms effectively differentiated cell types using multiple CMPs.
  • Increased number of CMPs led to improved cellular classification accuracy, irrespective of the MLA used.
  • The method demonstrated robustness when extended to include more cell lines.

Conclusions:

  • Multiple cellular mechanical phenotypes, analyzed by machine learning, provide a superior method for cell classification compared to CEP alone.
  • This MLA-based approach shows potential as an objective tool to assist in cancer diagnostics and prognostics.
  • Further research can explore broader applications of this technique in oncology.