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Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach.

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Bibliographic Details
Title: Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach.
Authors: Frank M; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510., Ni P; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510., Jensen M; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510., Gerstein MB; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510.; Department of Computer Science, Yale University, New Haven, CT 06511.; Department of Statistics and Data Science, Yale University, New Haven, CT 06511.
Source: Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Aug 13; Vol. 121 (33), pp. e2320510121. Date of Electronic Publication: 2024 Aug 07.
Publication Type: Journal Article
Journal Info: Publisher: National Academy of Sciences Country of Publication: United States NLM ID: 7505876 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1091-6490 (Electronic) Linking ISSN: 00278424 NLM ISO Abbreviation: Proc Natl Acad Sci U S A Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1091-6490
DOI:10.1073/pnas.2320510121