Machine Learning-Enhanced Estimation of Cellular Protein Levels from Bright-Field Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Cell Culture
2.2. Immunostaining
2.3. Imaging for AI Model
2.4. Image Processing and Machine Learning
2.5. Time-Lapse Imaging
2.6. Statistical Analysis
3. Results
3.1. Evaluation of Protein Estimation Accuracy Using Machine Learning
3.2. Applicability of the Protein Expression Estimation Model to Live NHEKs before Fixation
3.3. Integration of Machine Learning Models into Live Imaging
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Tohgasaki, T.; Touyama, A.; Kousai, S.; Imai, K. Machine Learning-Enhanced Estimation of Cellular Protein Levels from Bright-Field Images. Bioengineering 2024, 11, 774. https://doi.org/10.3390/bioengineering11080774
Tohgasaki T, Touyama A, Kousai S, Imai K. Machine Learning-Enhanced Estimation of Cellular Protein Levels from Bright-Field Images. Bioengineering. 2024; 11(8):774. https://doi.org/10.3390/bioengineering11080774
Chicago/Turabian StyleTohgasaki, Takeshi, Arisa Touyama, Shohei Kousai, and Kaita Imai. 2024. "Machine Learning-Enhanced Estimation of Cellular Protein Levels from Bright-Field Images" Bioengineering 11, no. 8: 774. https://doi.org/10.3390/bioengineering11080774