HyperHELM: Hyperbolic Hierarchy Encoding for mRNA Language Modeling

2025-09-29

AI Summary

Conventional language models for biological sequences rely on Euclidean geometry, which inadequately captures the intrinsic hierarchical structure between mRNA and amino acid sequences; although hyperbolic geometry excels at representing hierarchical relationships, it remains unexplored for mRNA language modeling. Method: We propose HyperHELM—a hybrid architecture featuring a Euclidean neural network backbone augmented with a hyperbolic space encoding layer and trained via hyperbolic masked language modeling. Contribution/Results: This is the first systematic integration of hyperbolic geometry into mRNA sequence language modeling. Evaluated across 10 downstream tasks on multi-species datasets, HyperHELM outperforms Euclidean baselines on 9 tasks, achieving an average 10% performance gain and a 3% improvement in antibody region annotation accuracy. Notably, it demonstrates superior robustness on long and low-GC sequences, empirically validating the efficacy of hyperbolic representations for hierarchical biological sequence modeling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Language models are increasingly applied to biological sequences like proteins and mRNA, yet their default Euclidean geometry may mismatch the hierarchical structures inherent to biological data. While hyperbolic geometry provides a better alternative for accommodating hierarchical data, it has yet to find a way into language modeling for mRNA sequences. In this work, we introduce HyperHELM, a framework that implements masked language model pre-training in hyperbolic space for mRNA sequences. Using a hybrid design with hyperbolic layers atop Euclidean backbone, HyperHELM aligns learned representations with the biological hierarchy defined by the relationship between mRNA and amino acids. Across multiple multi-species datasets, it outperforms Euclidean baselines on 9 out of 10 tasks involving property prediction, with 10% improvement on average, and excels in out-of-distribution generalization to long and low-GC content sequences; for antibody region annotation, it surpasses hierarchy-aware Euclidean models by 3% in annotation accuracy. Our results highlight hyperbolic geometry as an effective inductive bias for hierarchical language modeling of mRNA sequences.

Problem

• Modeling mRNA sequences with hierarchical structures using hyperbolic geometry
• Improving property prediction accuracy for mRNA sequences across species
• Enhancing generalization to out-of-distribution mRNA sequence characteristics

Innovation

• Hyperbolic space for mRNA language modeling
• Hybrid hyperbolic-Euclidean network architecture
• Hierarchy-aware mRNA representation learning