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Linguistics-based formalization of the antibody language as a basis for antibody language models.
Vu, Mai Ha; Robert, Philippe A; Akbar, Rahmad; Swiatczak, Bartlomiej; Sandve, Geir Kjetil; Haug, Dag Trygve Truslew; Greiff, Victor.
Affiliation
  • Vu MH; Department of Linguistics and Scandinavian Studies, University of Oslo, Oslo, Norway. m.h.vu@iln.uio.no.
  • Robert PA; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
  • Akbar R; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
  • Swiatczak B; Department of History of Science and Scientific Archeology, University of Science and Technology of China, Hefei, China.
  • Sandve GK; Department of Informatics, University of Oslo, Oslo, Norway.
  • Haug DTT; Department of Linguistics and Scandinavian Studies, University of Oslo, Oslo, Norway.
  • Greiff V; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway. victor.greiff@medisin.uio.no.
Nat Comput Sci ; 4(6): 412-422, 2024 Jun.
Article in En | MEDLINE | ID: mdl-38877120
ABSTRACT
Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable. Here we describe how a linguistic formalization of the antibody language, by characterizing its tokens and grammar, could address current challenges in antibody language model rule mining.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Linguistics / Antibodies Limits: Humans Language: En Journal: Nat Comput Sci Year: 2024 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Linguistics / Antibodies Limits: Humans Language: En Journal: Nat Comput Sci Year: 2024 Document type: Article Affiliation country: