Gene expression prediction from histology images via hypergraph neural networks.

ABSTRACT: Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to

SUBMITTER: Li B 

PROVIDER: S-EPMC11472757 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature