Protein function prediction is a challenging but important task in bioinformatics. Many prediction methods have been developed, but are still limited by the bottleneck on training sample quantity. Therefore, it is valuable to develop a data augmentation method that can generate high-quality synthetic samples to further improve the accuracy of prediction methods. In this work, we propose a novel generative adversarial networks-based method, FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples. The experimental results suggest that the synthetic protein feature samples are successful in improving the prediction accuracy for all three domains of Gene Ontology through augmentation of the original training protein feature samples.

蛋白质功能预测是生物信息学中一项具有挑战性但重要的任务。已经开发了许多预测方法，但是仍然受训练样本数量瓶颈的限制。因此，开发一种可以生成高质量合成样本以进一步提高预测方法准确性的数据增强方法非常有价值。在这项工作中，我们提出了一种基于生成对抗网络的新方法FFPred-GAN，以准确学习基于蛋白质序列的生物物理特征的高维分布，并生成高质量的合成蛋白质特征样本。