Arumalla, K.K.; Haince, J.-F.; Bux, R.A.; Huang, G.; Tappia, P.S.; Ramjiawan, B.; Ford, W.R.; Vaida, M.
Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status. Int. J. Mol. Sci. 2024, 25, 13029.
https://doi.org/10.3390/ijms252313029
AMA Style
Arumalla KK, Haince J-F, Bux RA, Huang G, Tappia PS, Ramjiawan B, Ford WR, Vaida M.
Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status. International Journal of Molecular Sciences. 2024; 25(23):13029.
https://doi.org/10.3390/ijms252313029
Chicago/Turabian Style
Arumalla, Kamala K., Jean-François Haince, Rashid A. Bux, Guoyu Huang, Paramjit S. Tappia, Bram Ramjiawan, W. Randolph Ford, and Maria Vaida.
2024. "Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status" International Journal of Molecular Sciences 25, no. 23: 13029.
https://doi.org/10.3390/ijms252313029
APA Style
Arumalla, K. K., Haince, J.-F., Bux, R. A., Huang, G., Tappia, P. S., Ramjiawan, B., Ford, W. R., & Vaida, M.
(2024). Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status. International Journal of Molecular Sciences, 25(23), 13029.
https://doi.org/10.3390/ijms252313029
