Integration of Raman tweezers and machine learning for label-free single-cell characterization of endometriosis cells
Abstract
). Random Forest and XGBoost for classifying VK2/E6E7 and A549 cells achieved over 80% accuracy without signs of overfitting. SHAP (SHapley Additive exPlanations) analysis highlighted lower lipid, amino acid, and amide III signals alongside higher saccharide signals as key drivers of cell differentiation. This is the first study to apply Raman tweezers for single-cell analysis of endometriosis cells, integrating deep-learning preprocessing and explainable machine learning. It offers a promising approach for probing endometriosis pathophysiology and supporting a less invasive diagnostic strategy.
Fields of Study & Subjects
Spectroscopy Techniques in Biomedical and Chemical Research
Advanced Biosensing Techniques and Applications
Cancer Research and Treatment
Publication details
- Journal / source
- The Analyst
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 151
- Issue
- 7
- Pages
- 1948–1959
- Publication date
- 1 January 2026
- Language
- EN