{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wu Y"],"funding":["Science and Technology Innovation Key R& Program of Chongqing","Science and Technology Innovation Key R&D Program of Chongqing","Science and Technology Innovation Key R& Program of Chongqing","National Natural Science Foundation of China","Science and Technology Innovation Key R&D Program of Chongqing","New Chongqing Youth Innovative Talents Project","National Key Research and Development Program of China"],"pagination":["bbaf422"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12365980"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(4)"],"pubmed_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"],"journal":["Briefings in bioinformatics"],"pubmed_title":["DANet: spatial gene expression prediction from H&E histology images through dynamic alignment."],"pmcid":["PMC12365980"],"funding_grant_id":["CSTB2024NSCQ-QCXMX0077","2022ZD0160400","CSTB2023TIAD-STX0001","62206031","62301092","CSTB2024TIAD-STX0032","62402071"],"pubmed_authors":["Cao J","Xie J","Nie J","Zeng Y","Wang Z","Wu Y"],"additional_accession":[]},"is_claimable":false,"name":"DANet: spatial gene expression prediction from H&E histology images through dynamic alignment.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-07-15T05:49:47.995Z","creation":"2026-06-29T03:19:35.79Z"},"accession":"S-EPMC12365980","cross_references":{"pubmed":["40833275"],"doi":["10.1093/bib/bbaf422"]}}