BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model
Adibvafa Fallahpour, Andrew Magnuson, Purav Gupta, Shihao Ma, Jack Naimer, Arnav Shah, Haonan Duan, Omar Ibrahim, Hani Goodarzi, Chris Maddison, Bo Wang
Unlocking deep, interpretable biological reasoning from complex genomic data is a paramount challenge for artificial intelligence, hindering critical scientific discovery. Existing DNA foundation models, despite their powerful sequence representation capabilities, often struggle with multi-step reasoning and lack inherent mechanisms for transparent, biologically intuitive explanations. We present BioReason, a pioneering architecture, that for the first time deeply integrates a DNA foundation model with a large language model (LLM). This novel connection empowers the LLM to directly process and reason with genomic information as a fundamental input modality, enabling a new form of multimodal biological understanding. BioReason's capacity for sophisticated, multi-step reasoning is cultivated through a regimen of supervised fine-tuning and targeted reinforcement learning, guiding the integrated system to generate logical and biologically coherent deductions. On challenging benchmarks, including KEGG-based disease pathway prediction—where BioReason improves accuracy by roughly 10 points (from 88% to 97%)—and variant effect analysis, BioReason demonstrates an average performance gain of 15% over strong single-modality baselines. A key breakthrough is BioReason's ability to reason over previously unseen biological entities and articulate its decision-making process through interpretable, step-by-step biological traces mechanistically supporting its predictions. BioReason offers a transformative approach for AI in biology, paving the way for deeper mechanistic insights and accelerated generation of testable hypotheses from genomic data.