通过双分支动态选择与重构预训练增强多模态蛋白质功能预测
Enhancing Multimodal Protein Function Prediction Through Dual-Branch Dynamic Selection with Reconstructive Pre-Training
AI总结：
本文提出DSRPGO模型，通过重构预训练、双向交互模块和动态选择模块增强多模态蛋白质功能预测，在人类数据集上显著优于基准模型。
摘要：
多模态蛋白质特征在蛋白质功能预测中起着至关重要的作用。然而，这些特征涵盖了广泛的信息，从结构数据和序列特征到蛋白质属性和交互网络，使得解读它们之间复杂的相互联系变得极具挑战性。在本工作中，我们提出了一种利用动态选择与重构预训练机制的多模态蛋白质功能预测方法（DSRPGO）。为了获取复杂的蛋白质信息，我们引入重构预训练来挖掘具有低语义层次的更细粒度信息。此外，我们提出了双向交互模块（BInM），以促进多模态特征之间的交互学习。另外，为了解决该任务中层次化多标签分类的困难，我们设计了一个动态选择模块（DSM），用于选择最有利于当前蛋白质功能预测的特征表示。我们提出的DSRPGO模型在人类数据集上的BPO、MFO和CCO指标上显著提升，从而优于其他基准模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
