Gene expression inference based on graph neural networks using L1000 data.

ABSTRACT: Gene expression profiles can serve as proxies for cellular states and provide valuable insights into the discovery of functional connections across diverse cellular contexts. A cost-effective method called L1000 has been developed to generate gene expression profiles for over a million different conditions. Since gene expression inference of this method relies on linear regression, nonlinear regression methods, including deep learning models, have been assessed. However, these approaches process gene expression data as a vector structure, motivating us to investigate whether nonlinear models based on a graph structure are more effective in capturing the relationships between genes underlying gene expression profiles. In this work, we show that the graph neural network (GNN) model with gene

SUBMITTER: Kim TH 

PROVIDER: S-EPMC12161499 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature