A Innovative Strategy for Identifying Subtypes Through the Analysis of Multi-Omics Data with Adversarial Autoencoders

Cancer is a disease that is both complex and diverse, and effective diagnosis and treatment require an accurate depiction of tumor subtypes. Traditional methods of cancer identification, which rely on clinical and histopathological criteria, have limitations in identifying key molecular subtypes. With the advancement of high-throughput genomics technologies, the field of cancer research has undergone a transformation, enabling detailed analysis of tumor molecular characteristics on a large scale. The integration of multiple types of genomic data is expected to provide a more comprehensive understanding of the molecular mechanisms of cancer and to promote the discovery of new diagnostic and therapeutic targets. However, achieving this requires the development of new computational techniques. In order to facilitate more efficient feature extraction and dimensionality reduction of multi-omics data, we present MultiDAAE (Multi-omics Double Adversarial Autoencoder), a novel technique that combines autoencoders with two discriminators to form two generative adversarial networks. On several cancer datasets, our method shows outstanding clustering performance when compared to state-of-the-art techniques. To sum up, MultiDAAE can help identify possible molecular pathways and provide information for the development of tailored cancer treatments.