AAAI 2026technical0 citations
TrinityDNA: A Bio-Inspired Foundational Model for Efficient Long-Sequence DNA Modeling
Qirong Yang, Yucheng Guo, Zicheng Liu, Yujie Yang, Qijin Yin, Siyuan Li, Shaomin Ji, Linlin Chao
Abstract
The modeling of genomic sequences presents unique challenges due to their long length and structural complexity. Traditional sequence models struggle to capture long-range dependencies and biological features inherent in DNA. In this work, we propose TrinityDNA, a novel DNA foundational model designed to address these challenges. The model integrates biologically informed components, including Groove Fusion for capturing DNA
BibTeX
@inproceedings{aaai2026_trinitydnaabioin,
title = {TrinityDNA: A Bio-Inspired Foundational Model for Efficient Long-Sequence DNA Modeling},
author = {Qirong Yang and Yucheng Guo and Zicheng Liu and Yujie Yang and Qijin Yin and Siyuan Li and Shaomin Ji and Linlin Chao and Xiaoming Zhang},
booktitle = {AAAI 2026},
year = {2026}
}