mlsanityData to 14 Oct 2026

HELM: Hierarchical Encoding for mRNA Language Modeling

arXiv 2410.12459 ↗First posted 16 Oct 2024cs.LG cs.CEcited by 10

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Abstract (arXiv)

Messenger RNA (mRNA) plays a crucial role in protein synthesis, with its codon structure directly impacting biological properties. While Language Models (LMs) have shown promise in analyzing biological sequences, existing approaches fail to account for the hierarchical nature of mRNA's codon structure. We introduce Hierarchical Encoding for mRNA Language Modeling (HELM), a novel pre-training strategy that incorporates codon-level hierarchical structure into language model training. HELM modulates the loss function based on codon synonymity, aligning the model's learning process with the biological reality of mRNA sequences. We evaluate HELM on diverse mRNA datasets and tasks, demonstrating that HELM outperforms standard language model pre-training as well as existing foundation model baselines on seven diverse downstream property prediction tasks and an antibody region annotation tasks on average by around 8%. Additionally, HELM enhances the generative capabilities of language model, producing diverse mRNA sequences that better align with the underlying true data distribution compared to non-hierarchical baselines.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 24 of its 79 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

24 of 79 references.

References · 24

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12 more
  1. 2021Deciphering antibody affinity maturation with language models and weakly supervised learningcited by 53
  2. 2024xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Proteincited by 50
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  4. 2024RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Taskscited by 33
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 2.

10 papers.