Cite
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.
MLA
Wilman, Wiktoria, et al. “Machine-Designed Biotherapeutics: Opportunities, Feasibility and Advantages of Deep Learning in Computational Antibody Discovery.” Briefings in Bioinformatics, vol. 23, no. 4, July 2022. EBSCOhost, https://doi.org/10.1093/bib/bbac267.
APA
Wilman, W., Wróbel, S., Bielska, W., Deszynski, P., Dudzic, P., Jaszczyszyn, I., Kaniewski, J., Młokosiewicz, J., Rouyan, A., Satława, T., Kumar, S., Greiff, V., & Krawczyk, K. (2022). Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Briefings in Bioinformatics, 23(4). https://doi.org/10.1093/bib/bbac267
Chicago
Wilman, Wiktoria, Sonia Wróbel, Weronika Bielska, Piotr Deszynski, Paweł Dudzic, Igor Jaszczyszyn, Jędrzej Kaniewski, et al. 2022. “Machine-Designed Biotherapeutics: Opportunities, Feasibility and Advantages of Deep Learning in Computational Antibody Discovery.” Briefings in Bioinformatics 23 (4). doi:10.1093/bib/bbac267.