Cite
Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling.
MLA
Altamirano-Flores, José Salomón, et al. “Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling.” Cells (2073-4409), vol. 12, no. 5, Mar. 2023, p. 772. EBSCOhost, https://doi.org/10.3390/cells12050772.
APA
Altamirano-Flores, J. S., Alvarado-Hernández, L. Á., Cuevas-Tello, J. C., Tino, P., Guerra-Palomares, S. E., & Garcia-Sepulveda, C. A. (2023). Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling. Cells (2073-4409), 12(5), 772. https://doi.org/10.3390/cells12050772
Chicago
Altamirano-Flores, José Salomón, Luis Ángel Alvarado-Hernández, Juan Carlos Cuevas-Tello, Peter Tino, Sandra E. Guerra-Palomares, and Christian A. Garcia-Sepulveda. 2023. “Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling.” Cells (2073-4409) 12 (5): 772. doi:10.3390/cells12050772.