A Machine Learning Approach to Identify Key Residues Involved in Protein–Protein Interactions Exemplified with SARS-CoV-2 Variants.

MLA

Quitté, Léopold, et al. “A Machine Learning Approach to Identify Key Residues Involved in Protein–Protein Interactions Exemplified with SARS-CoV-2 Variants.” International Journal of Molecular Sciences, vol. 25, no. 12, June 2024, p. 6535. EBSCOhost, https://doi.org/10.3390/ijms25126535.

APA

Quitté, L., Leclercq, M., Prunier, J., Scott-Boyer, M.-P., Moroy, G., & Droit, A. (2024). A Machine Learning Approach to Identify Key Residues Involved in Protein–Protein Interactions Exemplified with SARS-CoV-2 Variants. International Journal of Molecular Sciences, 25(12), 6535. https://doi.org/10.3390/ijms25126535

Chicago

Quitté, Léopold, Mickael Leclercq, Julien Prunier, Marie-Pier Scott-Boyer, Gautier Moroy, and Arnaud Droit. 2024. “A Machine Learning Approach to Identify Key Residues Involved in Protein–Protein Interactions Exemplified with SARS-CoV-2 Variants.” International Journal of Molecular Sciences 25 (12): 6535. doi:10.3390/ijms25126535.