# SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model

arXiv: https://arxiv.org/abs/2506.01833 · HTML: https://mlsanity.com/paper/2506.01833/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling](https://mlsanity.com/paper/2403.03234/) (2024)
- [BEND: Benchmarking DNA Language Models on biologically meaningful tasks](https://mlsanity.com/paper/2311.12570/) (2023)
- [Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning](https://mlsanity.com/paper/2411.02125/) (2024)
- [Regulatory DNA sequence Design with Reinforcement Learning](https://mlsanity.com/paper/2503.07981/) (2025)

## Built on by (most cited first, 5 of 5)

- [D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation](https://mlsanity.com/paper/2603.01780/) (2026)
- [MergeDNA: Context-aware Genome Modeling with Dynamic Tokenization through Token Merging](https://mlsanity.com/paper/2511.14806/) (2025)
- [NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction](https://mlsanity.com/paper/2511.02888/) (2025)
- [Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction](https://mlsanity.com/paper/2602.21550/) (2026)
- [GENEB: Why Genomic Models Are Hard to Compare](https://mlsanity.com/paper/2606.04525/) (2026)

## Abstract (arXiv)

Inspired by the success of unsupervised pre-training paradigms, researchers have applied these approaches to DNA pre-training. However, we argue that these approaches alone yield suboptimal results because pure DNA sequences lack sufficient information, since their functions are regulated by genomic profiles like chromatin accessibility. Here, we demonstrate that supervised training for genomic profile prediction serves as a more effective alternative to pure sequence pre-training. Furthermore, considering the multi-species and multi-profile nature of genomic profile prediction, we introduce our Species-Profile Adaptive Collaborative Experts (SPACE) that leverages Mixture of Experts (MoE) to better capture the relationships between DNA sequences across different species and genomic profiles, thereby learning more effective DNA representations. Through extensive experiments across various tasks, our model achieves state-of-the-art performance, establishing that DNA models trained with supervised genomic profiles serve as powerful DNA representation learners. The code is available at https://github.com/ZhuJiwei111/SPACE.