arXiv Machine Learning
From DNA Design to DNA Slimming: Auditable Agentic Discovery of a Deletion-Only Designer
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Locality-Controlled Out-of-Distribution Guidance for Robust Regulatory DNA Sequence Design
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Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery
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Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants
ARGUS is an evidence‑constrained framework that separates deterministic biological computation from large‑language‑model reasoning to interpret single nucleotide variants in noncoding regulatory regions. It uses 458 DNABERT‑based transcription factor binding models within a hypothesis‑directed loop where a planner selects evidence sources, a verifier interprets observations, and the investigation path adapts to uncertainty. In a case study of variant rs6983267, ARGUS generated divergent investigative trajectories for four transcription factors, demonstrating how real experimental data from ADASTRA, JASPAR, and ENCODE cCRE guide conclusions and how the LLM planner can reduce unnecessary tool calls.
arXiv Machine Learning
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EvoLen: Evolution-Guided Tokenization for DNA Language Model
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