Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations.

ABSTRACT:

Motivation

Variational autoencoders (VAEs) have rapidly increased in popularity in biological applications and have already successfully been used on many omic datasets. Their latent space provides a low-dimensional representation of input data, and VAEs have been applied, e.g. for clustering of single-cell transcriptomic data. However, due to their non-linear nature, the patterns that VAEs learn in the latent space remain obscure. Hence, the lower-dimensional data embedding cannot directly be related to input features.

Results

To shed light on the inner workings of VAE and enable direct interpretability of the model through its structure, we designed a novel VAE, OntoVAE (Ontology guided VAE) that can incorporate any ontology in its latent space and decoder part and, thus,

SUBMITTER: Doncevic D 

PROVIDER: S-EPMC10301695 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature