Year: 2025
Type: preprint
Abstract: Inspired by the success of unsupervised pre-training paradigms, researchers have applied these approaches to DNA pre-training. However, we argue that these approaches alone yield suboptimal results because pure DNA sequences lack sufficient information, since their functions are regulated by genomic profiles like chromatin accessibility. Here, we demonstrate that supervised training for genomic profile prediction serves as a more effective alternative to pure sequence pre-training. Furthermore, considering the multi-species and multi-profile nature of genomic profile prediction, we introduce our $\textbf{S}$pecies-$\textbf{P}$rofile $\textbf{A}$daptive $\textbf{C}$ollaborative $\textbf{E}$xperts (SPACE) that leverages Mixture of Experts (MoE) to better capture the relationships between DNA sequences across different species and genomic profiles, thereby learning more effective DNA representations. Through extensive experiments across various tasks, our model achieves state-of-the-art performance, establishing that DNA models trained with supervised genomic profiles serve as powerful DNA representation learners. The code is available at https://github.com/ZhuJiwei111/SPACE.
Source: arXiv (Cornell University)
Authors: Yang, Zhao, Zhu, Jiwei, Su, Bing
Institution: Renmin University of China
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G7092948576, G8415418894
OpenAlex ID: W4414898179
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