BERT-GA framework optimizes therapeutic mRNA sequences to CAI 0.73-0.74, tAI 0.63-0.64, balanced MFE around -350 kcal/mol, and reduced CpG/UpA motifs, claiming better equilibrium than Linear Design or BiLSTM-CRF.
A new deep-learning-based approach for mrna optimization: High fidelity, computation efficiency, and multiple optimization factors,
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An Evolutionary Approach for Designing Stable and Highly Expressible Low-Immunogenicity Therapeutic mRNA Sequences
BERT-GA framework optimizes therapeutic mRNA sequences to CAI 0.73-0.74, tAI 0.63-0.64, balanced MFE around -350 kcal/mol, and reduced CpG/UpA motifs, claiming better equilibrium than Linear Design or BiLSTM-CRF.