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Leveraging a large language model to predict protein phase transition: a physical, multiscale and interpretable approach
Mor Frank, Pengyu Ni, Matthew Jensen, Mark B Gerstein
doi: https://doi.org/10.1101/2023.11.21.568125
Mor Frank
aDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510, USA
bProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
Pengyu Ni
aDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510, USA
bProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
Matthew Jensen
aDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510, USA
bProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
Mark B Gerstein
aDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06510, USA
bProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
cDepartment of Computer Science, Yale University, New Haven, CT 06511, USA
dDepartment of Statistics and Data Science, Yale University, New Haven, CT 06511, USA
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Posted July 10, 2024.
Leveraging a large language model to predict protein phase transition: a physical, multiscale and interpretable approach
Mor Frank, Pengyu Ni, Matthew Jensen, Mark B Gerstein
bioRxiv 2023.11.21.568125; doi: https://doi.org/10.1101/2023.11.21.568125
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