arXiv:2505.23579cs.LG
BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model
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Abstract
Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel at representing sequences, they struggle with multi-step reasoning and lack transparent, biologically meaningful explanations. BioReason addresses this by tightly integrating a DNA foundation model with a large language model (LLM), enabling the LLM to directly interpret and reason over genomic information. Through supervised fine-tuning and reinforcement learning, BioReason learns to produce logical, biologically coherent deductions. It achieves major performance gains, boosting KEGG-based disease pathway prediction accuracy from 86% to 98% and improving variant effect prediction by an average of 15% over strong baselines. BioReason can reason over unseen biological entities and explain its decisions step by step, offering a transformative framework for interpretable, mechanistic AI in biology. All data, code, and checkpoints are available at https://github.com/bowang-lab/BioReason
We built BioReason - integrating DNA foundation models (Evo2) with LLMs (Qwen3) for biological reasoning. - 97% accuracy on disease pathway prediction - 80-88% on variant effect prediction - 15%+ gains over DNA-only or LLM-only models - Tested on 87K+ genomic variants from…
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