DANet: spatial gene expression prediction from H&E histology images through dynamic alignment.

ABSTRACT: Predicting spatial gene expression from Hematoxylin and Eosin histology images offers a promising approach to significantly reduce the time and cost associated with gene expression sequencing, thereby facilitating a deeper understanding of tissue architecture and disease mechanisms. Achieving accurate gene expression prediction requires the extraction of highly refined features from pathological images; however, existing methods often struggle to effectively capture fine-grained local details and model gene-gene correlations. Moreover, in bimodal contrastive learning, dynamically and efficiently aligning heterogeneous modalities remains a critical challenge. To address these issues, we propose a novel method for predicting gene expression. First, we introduce a dense connective structure t

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12365980 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature