AEGAN-Pathifier: A data augmentation method to improve cancer classification for imbalanced gene expression data

Abstract Background: Cancer classification has consistently been a challenging problem, with the main difficulties being high-dimensional data and the collection of patient samples. Concretely, obtaining patient samples is a costly and resource-intensive process, and imbalances often exist between samples. Moreover, expression data is characterized by high dimensionality, small samples and high noise, which could easily lead to struggles such as dimensionality catastrophe and overfitting. Thus, we incorporate prior knowledge from the pathway and combine AutoEncoder and Generative Adversarial Networks (GANs) to solve these difficulties. Results: In this study, we propose an effective and efficient deep learning method, called AEGAN, for generating synthetic samples of the minority class in imbalanced gene expression data. The proposed data balancing technique has been demonstrated to be highly useful for cancer classification and improving the performance of classifier models. Additionally, we integrate prior knowledge from the pathway and employ the pathifier algorithm to calculate pathway scores for each sample. This data augmentation approach, referred to as AEGAN-Pathifier, not only preserves the biological features of the data but also possesses dimensionality reduction capabilities. Through extensive validation with various classifiers, the experimental results consistently show an improvement in classifier performance. Conclusion: AEGAN-Pathifier demonstrates superior performance on all three imbalanced datasets: GSE25066, GSE20194, and Liver24. The results obtain with various classifiers strongly indicate the remarkable generalizability of AEGAN-Pathifier, making it easily applicable to other tasks.