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

arXiv:2405.09756v1 Announce Type: new
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 …

abstract adversarial art arxiv autoencoder biology class classification cs.lg cs.ne data diagnostics equipment generated generative generative adversarial networks integration machine machine learning medical networks q-bio.gn research sequencing state type