NaNa和MiGu：增强图神经网络中蛋白质分类的语义数据增强技术
NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks
AI总结：
针对现有方法在蛋白质分类中忽略动态构象和侧链特征的问题，提出NaNa和MiGu语义数据增强方法及共嵌入残差学习框架，融合主链化学与侧链生物物理信息，显著提升GIN在EC和Fold数据集上的分类性能。
摘要：
蛋白质分类任务在药物发现中至关重要。现实世界的蛋白质结构是动态的，这将决定蛋白质的性质。然而，现有的机器学习方法，如ProNet（Wang et al., 2022a），仅获取有限的构象特征和蛋白质侧链特征，导致其预测中的蛋白质结构不切实际且蛋白质类别不准确。在本文中，我们提出了新颖的语义数据增强方法，即新节点属性的新颖增强（NaNa）和分子相互作用与几何升级（MiGu），以将主链化学和侧链生物物理信息纳入蛋白质分类任务和共嵌入残差学习框架中。具体而言，我们利用蛋白质的分子生物物理、二级结构、化学键和离子特征来促进蛋白质分类任务。此外，我们的语义增强方法和共嵌入残差学习框架可将GIN（Xu et al., 2019）在EC和Fold数据集（Bairoch, 2000; Andreeva et al., 2007）上的性能分别提高16.41%和11.33%。我们的代码可在https://github.com/r08b46009/Code_for_MIGU_NANA/tree/main获取。
英文摘要：
Protein classification tasks are essential in drug discovery. Real-world protein structures are dynamic, which will determine the properties of proteins. However, the existing machine learning methods, like ProNet (Wang et al., 2022a), only access limited conformational characteristics and protein side-chain features, leading to impractical protein structure and inaccuracy of protein classes in their predictions. In this paper, we propose novel semantic data augmentation methods, Novel Augmentation of New Node Attributes (NaNa), and Molecular Interactions and Geometric Upgrading (MiGu) to incorporate backbone chemical and side-chain biophysical information into protein classification tasks and a co-embedding residual learning framework. Specifically, we leverage molecular biophysical, secondary structure, chemical bonds, and ionic features of proteins to facilitate protein classification tasks. Furthermore, our semantic augmentation methods and the co-embedding residual learning framework can improve the performance of GIN (Xu et al., 2019) on EC and Fold datasets (Bairoch, 2000; Andreeva et al., 2007) by 16.41% and 11.33% respectively. Our code is available at https://github.com/r08b46009/Code_for_MIGU_NANA/tree/main.
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。
