[2412.05430] DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA

Computer Science > Machine Learning arXiv:2412.05430 (cs) [Submitted on 6 Dec 2024 (v1), last revised 23 Mar 2026 (this version, v3)] Title:DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA Authors:Aman Patel, Arpita Singhal, Austin Wang, Anusri Pampari, Maya Kasowski, Anshul Kundaje View a PDF of the paper titled DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA, by Aman Patel and 5 other authors View PDF HTML (experimental) Abstract:Recent advances in self-supervised models for natural language, vision, and protein sequences have inspired the development of large genomic DNA language models (DNALMs). These models aim to learn generalizable representations of diverse DNA elements, potentially enabling various genomic prediction, interpretation and design tasks. Despite their potential, existing benchmarks do not adequately assess the capabilities of DNALMs on key downstream applications involving an important class of non-coding DNA elements critical for regulating gene activity. In this study, we introduce DART-Eval, a suite of representative benchmarks specifically focused on regulatory DNA to evaluate model performance across zero-shot, probed, and fine-tuned scenarios against contemporary ab initio models as baselines. Our benchmarks target biologically meaningful downstream tasks such as functional sequence feature discovery, predicting cell-type specific regulatory activity, and counterfactual pre...