Biological Reasoning-Informed Regression for Interpretable Regulatory DNA Activity Prediction

2026-06-06

AI Summary

This study addresses the limitations of existing methods in predicting cis-regulatory element (CRE) activity from DNA sequences, which often suffer from insufficient accuracy and lack of interpretability. The authors propose R3LM, a novel framework that introduces CRE-ReasonBench—the first dataset incorporating mechanistic reasoning trajectories—and employs a two-stage training strategy to integrate structured biological knowledge into a large language model (LLM) for reasoning-based regression. Evaluated across three cell types, R3LM significantly outperforms both sequence-only LLMs and specialized DNA models, achieving state-of-the-art performance in enhancer activity prediction while simultaneously providing interpretable insights into the underlying regulatory mechanisms. This approach thus bridges high predictive accuracy with biologically meaningful interpretability.

Abstract

DNA cis-regulatory elements (CREs) such as enhancers control gene expression levels. Accurately predicting regulatory activity from DNA sequences is valuable but challenging, as it requires understanding complex biological regulatory processes. Existing methods typically regress activity scores from sequences in a black-box manner, limiting both interpretability and regression performance. Meanwhile, large language models (LLMs) benefit from explicit reasoning processes, yet directly applying LLMs to raw DNA sequences performs poorly. In this paper, we bridge this gap by introducing R3LM, a framework that teaches LLMs reasoning-informed regression on regulatory DNA through structured biological knowledge. Specifically, we design a biologically grounded data format that structures DNA's regulatory information for improved LLM understanding, and construct CRE-ReasonBench, the first dataset that associates DNA sequences and activity scores with mechanistic reasoning traces. Through two-stage training that first teaches LLMs reasoning over structured biological information then performs regression, R3LM achieves state-of-the-art performance on enhancer prediction across three cell types, outperforming both LLMs with raw sequence input and specialized DNA models while providing interpretable mechanistic explanations. We expect R3LM as an interpretable reward model that can effectively assist biologists in CRE design. Code is available at https://github.com/DuanYi516/R3LM.

Problem

• regulatory DNA activity prediction
• interpretability
• cis-regulatory elements
• enhancer prediction
• biological reasoning

Innovation

• biological reasoning
• interpretable regression
• large language models
• cis-regulatory elements
• structured biological knowledge