Bridging peptide presentation and T cell recognition with multi-task learning

IF 23.9 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Nature Machine Intelligence Pub Date : 2025-02-20 DOI:10.1038/s42256-025-01004-y
Li Su, Duolin Wang, Dong Xu
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Abstract

The immunogenic binding interactions of antigens are complex and interconnected. A new transformer-based model can simultaneously predict the bindings of antigens to two main receptors.

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用多任务学习架起肽呈递和T细胞识别的桥梁
抗原的免疫原性结合相互作用是复杂且相互关联的。一种新的基于转换器的模型可以同时预测抗原与两种主要受体的结合。
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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