Update README.md

README.md

@@ -91,12 +91,12 @@ If you use this work, please cite:
 	year = {2025},
 	doi = {10.1101/2025.08.19.671089},
 	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Codon language models offer a promising framework for modeling protein-coding DNA sequences, yet current approaches often conflate codon usage with amino acid semantics, limiting their ability to capture DNA-level biology. We introduce SynCodonLM, a codon language model that enforces a biologically grounded constraint: masked codons are only predicted from synonymous options, guided by the known protein sequence. This design disentangles codon-level from protein-level semantics, enabling the model to learn nucleotide-specific patterns. The constraint is implemented by masking non-synonymous codons from the prediction space prior to softmax. Unlike existing models, which cluster codons by amino acid identity, SynCodonLM clusters by nucleotide properties, revealing structure aligned with DNA-level biology. Furthermore, SynCodonLM outperforms existing models on 6 of 7 benchmarks sensitive to DNA-level features, including mRNA and protein expression. Our approach advances domain-specific representation learning and opens avenues for sequence design in synthetic biology, as well as deeper insights into diverse bioprocesses.Competing Interest StatementThe authors have declared no competing interest.},
 	URL = {https://www.biorxiv.org/content/early/2025/08/24/2025.08.19.671089},
 	eprint = {https://www.biorxiv.org/content/early/2025/08/24/2025.08.19.671089.full.pdf},
 	journal = {bioRxiv}
 }
 ```
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 ## Usage With Batches
 ```python