Enhancing Protein Predictive Models via Proteins Data Augmentation: A
  Benchmark and New Directions
Rui Sun Lirong Wu Haitao Lin Yufei Huang Stan Z. Li
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.