Exploring generative deep learning for omics data using log-linear models.

ABSTRACT:

Motivation

Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for uncovering structure by generating synthetic samples. However, exploration and visualization is not as straightforward as with image applications.

Results

We demonstrate how log-linear models, fitted to the generated, synthetic data can be used to extract patterns from omics data, learned by deep generative techniques. Specifically, interactions between latent representations learned by the approaches and generated synthetic data are used to determine sets of joint patterns. Distances of patterns with respect to the distribution of la

SUBMITTER: Hess M 

PROVIDER: S-EPMC7755415 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature