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

TL;DR

This paper introduces a neural network model combining autoencoders and GANs to address high-dimensional, imbalanced multi-omics data, improving cancer classification accuracy.

Contribution

The study presents a novel approach integrating autoencoders and GANs for dimensionality reduction and synthetic data generation in multi-omics cancer classification.

Findings

Achieved 95.09% accuracy on bladder cancer dataset.

Achieved 88.82% accuracy on breast cancer dataset.

Outperformed existing models in classification tasks.

Abstract

In the relentless efforts in enhancing medical diagnostics, the integration of state-of-the-art machine learning methodologies has emerged as a promising research area. In molecular biology, there has been an explosion of data generated from multi-omics sequencing. The advent sequencing equipment can provide large number of complicated measurements per one experiment. Therefore, traditional statistical methods face challenging tasks when dealing with such high dimensional data. However, most of the information contained in these datasets is redundant or unrelated and can be effectively reduced to significantly fewer variables without losing much information. Dimensionality reduction techniques are mathematical procedures that allow for this reduction; they have largely been developed through statistics and machine learning disciplines. The other challenge in medical datasets is having an imbalanced number of samples in the classes, which leads to biased results in machine learning models. 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. Latent space is the reduced dimensional space that captures the meaningful features of the original data. Our model starts with feature selection to select the discriminative features before feeding them to the neural network. Then, the model predicts the outcome of cancer for different datasets. The proposed model outperformed other existing models by scoring accuracy of 95.09% for bladder cancer dataset and 88.82% for the breast cancer dataset.