Biological Reasoning-Informed Regression for Interpretable Regulatory DNA Activity Prediction
arXiv:2606.08147v1 Announce Type: cross 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 p
This research addresses the current limitations of black-box AI methods in biological sequence prediction and the poor performance of direct LLM application to DNA, leveraging new AI advancements to bridge this gap.
Improving the interpretability and accuracy of regulatory DNA activity prediction will accelerate drug discovery, gene editing, and our fundamental understanding of biological systems.
The development of biological reasoning-informed AI models moves beyond opaque predictions, enabling more targeted and reliable manipulation of gene expression and advancing synthetic biology applications.
- · Biotechnology sector
- · Pharmaceutical R&D
- · AI researchers in biology
- · Synthetic biology companies
- · Traditional drug screening methods
- · Black-box AI model developers for genomics
More efficient and predictable development of gene therapies and genetically engineered organisms will emerge.
The ability to precisely design and control biological functions could lead to new industries based on programmable biology.
Enhanced understanding of human health at a genetic level may fundamentally alter disease prevention and treatment paradigms.
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Read at arXiv cs.LG