An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification

no code implementations • 16 May 2024 • Ibrahim Al-Hurani, Abedalrhman Alkhateeb, Salama Ikki

This study, focused on tackling these challenges in a neural network that incorporates autoencoder to extract latent space of the features, and Generative Adversarial Networks (GAN) to generate synthetic samples.

Dimensionality Reduction feature selection