AI RESEARCH
DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA
arXiv CS.LG
•
ArXi:2412.05430v3 Announce Type: replace 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.