Augmentation is an effective alternative to utilize the small amount of
labeled protein data. However, most of the existing work focuses on design-ing
new architectures or pre-training tasks, and relatively little work has studied
data augmentation for proteins. This paper extends data augmentation techniques
previously used for images and texts to proteins and then benchmarks these
techniques on a variety of protein-related tasks, providing the first
comprehensive evaluation of protein augmentation. Furthermore, we propose two
novel semantic-level protein augmentation methods, namely Integrated Gradients
Substitution and Back Translation Substitution, which enable protein
semantic-aware augmentation through saliency detection and biological
knowledge. Finally, we integrate extended and proposed augmentations into an
augmentation pool and propose a simple but effective framework, namely
Automated Protein Augmentation (APA), which can adaptively select the most
suitable augmentation combinations for different tasks. Extensive experiments
have shown that APA enhances the performance of five protein related tasks by
an average of 10.55% across three architectures compared to vanilla
implementations without augmentation, highlighting its potential to make a
great impact on the field.

数据增强技术被用于蛋白质数据中，通过引入两种新的语义级别蛋白质增强方法，以及将增强技术整合到一个自适应选择增强组合的框架中，通过实验证明 Automated Protein Augmentation（APA）提高了三种架构下五个蛋白质相关任务的性能，平均提升了 10.55%，显示出其在该领域具有巨大潜力。