TY  - JOUR
A1  - Li, Sizhen
A1  - Moayedpour, Saeed
A1  - Li, Ruijiang
A1  - Bailey, Michael
A1  - Riahi, Saleh
A1  - Kogler-Anele, Lorenzo
A1  - Miladi, Milad
A1  - Miner, Jacob
A1  - Pertuy, Fabien
A1  - Zheng, Dinghai
A1  - Wang, Jun
A1  - Balsubramani, Akshay
A1  - Tran, Khang
A1  - Zacharia, Minnie
A1  - Wu, Monica
A1  - Gu, Xiaobo
A1  - Clinton, Ryan
A1  - Asquith, Carla
A1  - Skaleski, Joseph
A1  - Boeglin, Lianne
A1  - Chivukula, Sudha
A1  - Dias, Anusha
A1  - Strugnell, Tod
A1  - Montoya, Fernando Ulloa
A1  - Agarwal, Vikram
A1  - Bar-Joseph, Ziv
A1  - Jager, Sven
T1  - CodonBERT large language model for mRNA vaccines
Y1  - 2024/07/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1027 
EP  - 1035 
DO  - 10.1101/gr.278870.123 
VL  - 34 
IS  - 7 
UR  - http://genome.cshlp.org/content/34/7/1027.abstract 
N2  - mRNA-based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encoded by an enormously large number of mRNAs. The actual mRNA sequence can have a large impact on several properties, including expression, stability, immunogenicity, and more. To enable the selection of an optimal sequence, we developed CodonBERT, a large language model (LLM) for mRNAs. Unlike prior models, CodonBERT uses codons as inputs, which enables it to learn better representations. CodonBERT was trained using more than 10 million mRNA sequences from a diverse set of organisms. The resulting model captures important biological concepts. CodonBERT can also be extended to perform prediction tasks for various mRNA properties. CodonBERT outperforms previous mRNA prediction methods, including on a new flu vaccine data set. 
ER  -