Benchmarking variational AutoEncoders on cancer transcriptomics data.

ABSTRACT: Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream tasks of cluster agreement with cancer subtypes and survival analysis. We studied the effect of the latent space dimensionality, learning rate, optimizer, initialization and activati

SUBMITTER: Eltager M 

PROVIDER: S-EPMC10553230 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature