Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

Abstract Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging...

Full description