ScGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data.

ABSTRACT:

Background

Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction networks and gene regulatory networks, provide insights into the molecular basis of cellular processes and often form functional clusters in different tissue and disease contexts.

Results

We present scGraph2Vec, a deep learning framework for generating informative gene embeddings. scGraph2Vec extends the variational graph autoencoder framework and integrates single-cell datasets and gene-gene interaction networks. We demonstrate that the gene embeddings are biologically interpretable and enable the identification of gene clusters re

SUBMITTER: Lin S 

PROVIDER: S-EPMC11659981 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature