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 Network (GAN) to solve these difficulties.

Results

In this study, we propose an effective and efficient deep learning method, named AEGAN, which combines the capabilities of AutoEncoder and GAN to

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC11673641 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature