jheuschkel/cds-dataset

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The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at b437922
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Citation text from the card

@article {Heuschkel2025.08.19.671089,
	author = {Heuschkel, James and Kingsley, Laura and Pefaur, Noah and Nixon, Andrew and Cramer, Steven},
	title = {Advancing Codon Language Modeling with Synonymous Codon Constrained Masking},
	elocation-id = {2025.08.19.671089},
	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

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dataset_licence({ "id": "jheuschkel/cds-dataset" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0