文献类型：期刊文献

英文题名：DRFormer: A Benchmark Model for RNA Sequence Downstream Tasks

作者：Fu, Jianqi[1];Li, Haohao[2];Kang, Yanlei[1];Zhu, Hancan[3];Huang, Tiren[2];Li, Zhong[1]

机构：[1]Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China;[2]Zhejiang Sci Tech Univ, Coll Sci, Hangzhou 310018, Peoples R China;[3]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Peoples R China

年份：2025

卷号：16

期号：3

外文期刊名：GENES

收录：SCI-EXPANDED(收录号：WOS:001452101300001)、、Scopus(收录号：2-s2.0-105001270549)、WOS

基金：This work is supported by the National Natural Science Foundation of China under Grant No. U24A20249 and 12171434, and Science and Technology Plan Project of Huzhou City, China (no. 2022GZ51).

语种：英文

外文关键词：RNA; RSS; RBP; sequence classification; multimodal

外文摘要：Background/Objectives: RNA research is critical for understanding gene regulation, disease mechanisms, and therapeutic development. Constructing effective RNA benchmark models for accurate downstream analysis has become a significant research challenge. The objective of this study is to propose a robust benchmark model, DRFormer, for RNA sequence downstream tasks. Methods: The DRFormer model utilizes RNA sequences to construct novel vision features based on secondary structure and sequence distance. These features are pre-trained using the SWIN model to develop a SWIN-RNA submodel. This submodel is then integrated with an RNA sequence model to construct a multimodal model for downstream analysis. Results: We conducted experiments on various RNA downstream tasks. In the sequence classification task, the MCC reached 94.4%, surpassing the state-of-the-art RNAErnie model by 1.2%. In the protein-RNA interaction prediction, DRFormer achieved an MCC of 0.492, outperforming advanced models like BERT-RBP and PrismNet. In RNA secondary structure prediction, the F1 score was 0.690, exceeding the widely used SPOT-RNA model by 1%. Additionally, generalization experiments on DNA tasks yielded satisfactory results. Conclusions: DRFormer is the first RNA sequence downstream analysis model that leverages structural features to construct a vision model and integrates sequence and vision models in a multimodal manner. This approach yields excellent prediction and analysis results, making it a valuable contribution to RNA research.