biostudies-literatureWu YScience and Technology Innovation Key R& Program of ChongqingScience and Technology Innovation Key R&D Program of ChongqingScience and Technology Innovation Key R& Program of ChongqingNational Natural Science Foundation of ChinaScience and Technology Innovation Key R&D Program of ChongqingNew Chongqing Youth Innovative Talents ProjectNational Key Research and Development Program of Chinabbaf422https://www.ebi.ac.uk/biostudies/studies/S-EPMC12365980biostudies-literatureUnknown26(4)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 tBriefings in bioinformaticsDANet: spatial gene expression prediction from H&E histology images through dynamic alignment.PMC12365980CSTB2024NSCQ-QCXMX00772022ZD0160400CSTB2023TIAD-STX00016220603162301092CSTB2024TIAD-STX003262402071Cao JXie JNie JZeng YWang ZWu YfalseDANet: spatial gene expression prediction from H&E histology images through dynamic alignment.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 t2025-01-01T00:00:00Z2025 Jul2026-07-15T05:49:47.995Z2026-06-29T03:19:35.79ZS-EPMC123659804083327510.1093/bib/bbaf422