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Bridging the neutralization gap for unseen antibodies., и . Nat. Mac. Intell., 5 (1): 8-10 (января 2023)Linguistically inspired roadmap for building biologically reliable protein language models., , , , , , и . Nat. Mac. Intell., 5 (5): 485-496 (мая 2023)Best practices for machine learning in antibody discovery and development., , , , и . CoRR, (2023)ImmunoLingo: Linguistics-based formalization of the antibody language., , , , , , и . CoRR, (2022)Hopfield Networks is All You Need, , , , , , , , , и 6 other автор(ы). (2020)cite arxiv:2008.02217Comment: 10 pages (+ appendix); 12 figures; Blog: https://ml-jku.github.io/hopfield-layers/; GitHub: https://github.com/ml-jku/hopfield-layers.Comparison of methods for phylogenetic B-cell lineage inference using time-resolved antibody repertoire simulations (AbSim)., , , , , , , , и . Bioinform., 33 (24): 3938-3946 (2017)AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation., , , , , , , , , и 1 other автор(ы). CoRR, (2022)Advancing protein language models with linguistics: a roadmap for improved interpretability., , , , , , и . CoRR, (2022)TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences., , , , , , , , и . Briefings Bioinform., (2022)Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery., , , , , , , , , и 3 other автор(ы). Briefings Bioinform., (2022)