# BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models

arXiv: https://arxiv.org/abs/2506.08936 · HTML: https://mlsanity.com/paper/2506.08936/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [Multi-modal Transfer Learning between Biological Foundation Models](https://mlsanity.com/paper/2406.14150/) (2024)
- [Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification](https://mlsanity.com/paper/2502.07299/) (2025)

## Built on by (most cited first, 2 of 2)

- [GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction](https://mlsanity.com/paper/2509.21971/) (2025)
- [HyperHELM: Hyperbolic Hierarchy Encoding for mRNA Language Modeling](https://mlsanity.com/paper/2509.24655/) (2025)

## Abstract (arXiv)

We present BioLangFusion, a simple approach for integrating pre-trained DNA, mRNA, and protein language models into unified molecular representations. Motivated by the central dogma of molecular biology (information flow from gene to transcript to protein), we align per-modality embeddings at the biologically meaningful codon level (three nucleotides encoding one amino acid) to ensure direct cross-modal correspondence. BioLangFusion studies three standard fusion techniques: (i) codon-level embedding concatenation, (ii) entropy-regularized attention pooling inspired by multiple-instance learning, and (iii) cross-modal multi-head attention -- each technique providing a different inductive bias for combining modality-specific signals. These methods require no additional pre-training or modification of the base models, allowing straightforward integration with existing sequence-based foundation models. Across five molecular property prediction tasks, BioLangFusion outperforms strong unimodal baselines, showing that even simple fusion of pre-trained models can capture complementary multi-omic information with minimal overhead.