BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918169774391296 |
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| author | Fallahpour, Adibvafa Magnuson, Andrew Gupta, Purav Ma, Shihao Naimer, Jack Shah, Arnav Duan, Haonan Ibrahim, Omar Goodarzi, Hani Maddison, Chris J. Wang, Bo |
| author_facet | Fallahpour, Adibvafa Magnuson, Andrew Gupta, Purav Ma, Shihao Naimer, Jack Shah, Arnav Duan, Haonan Ibrahim, Omar Goodarzi, Hani Maddison, Chris J. Wang, Bo |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23579 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model Fallahpour, Adibvafa Magnuson, Andrew Gupta, Purav Ma, Shihao Naimer, Jack Shah, Arnav Duan, Haonan Ibrahim, Omar Goodarzi, Hani Maddison, Chris J. Wang, Bo Machine Learning J.3; I.2 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 |
| title | BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model |
| topic | Machine Learning J.3; I.2 |
| url | https://arxiv.org/abs/2505.23579 |