RNop uses four custom loss functions and a vision-transformer encoder to optimize mRNA codons for fidelity, codon adaptation, tRNA availability, and secondary structure, with incompletely evidenced expression claims.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-bio.QM 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors
RNop uses four custom loss functions and a vision-transformer encoder to optimize mRNA codons for fidelity, codon adaptation, tRNA availability, and secondary structure, with incompletely evidenced expression claims.