通过动态选择与重建预训练，提升多模态蛋白功能预测精度
Enhancing Multimodal Protein Function Prediction Through Dual-Branch Dynamic Selection with Reconstructive Pre-Training
- 引入双向交互模块，实现多模态特征协同学习
- 动态选择模块显著提升BPO/MFO/CCO三项指标
- 适合蛋白质功能注释与药物靶点发现研究者
多模态蛋白特征在蛋白功能预测中至关重要，但其涵盖结构、序列、属性及互作网络等复杂信息，难以解析其深层关联。本文提出DSRPGO方法，结合动态选择与重建预训练机制。通过重建预训练挖掘低语义层次的细粒度信息，并设计双向交互模块（BInM）促进多模态特征间交互学习。针对层级多标签分类难题，引入动态选择模块（DSM），自适应选择最优特征表示。在人类数据集上，该模型在BPO、MFO和CCO三项指标上均显著优于现有基准模型。
原文摘要 · Abstract (English)
Multimodal protein features play a crucial role in protein function prediction. However, these features encompass a wide range of information, ranging from structural data and sequence features to protein attributes and interaction networks, making it challenging to decipher their complex interconnections. In this work, we propose a multimodal protein function prediction method (DSRPGO) by utilizing dynamic selection and reconstructive pre-training mechanisms. To acquire complex protein information, we introduce reconstructive pre-training to mine more fine-grained information with low semantic levels. Moreover, we put forward the Bidirectional Interaction Module (BInM) to facilitate interactive learning among multimodal features. Additionally, to address the difficulty of hierarchical multi-label classification in this task, a Dynamic Selection Module (DSM) is designed to select the feature representation that is most conducive to current protein function prediction. Our proposed DSRPGO model improves significantly in BPO, MFO, and CCO on human datasets, thereby outperforming other benchmark models.
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