发表机构
Department of Mechanical Engineering, National Institute of Technology Durgapur; Department of Electrical Engineering, National Institute of Technology Durgapur; Research Center, Skoda Auto University; Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove; Department of Computer Science and Engineering, Jadavpur University（机械工程系，杜尔加布尔国立技术学院; 电气工程系，杜尔加布尔国立技术学院; 斯柯达汽车大学研究中心; 赫拉德茨克拉洛韦大学信息与管理学院基础与应用研究中心; 计算机科学与工程系，贾达普大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在从H&E组织学预测空间基因表达，提出双图架构HierarchicalDAEW，通过域感知边缘加权卷积和基因级图融合先验与共表达，结合证据不确定性估计处理可靠性，实验表明其与真实表达相关性强，组件必要且能识别低置信度预测。
AI 中文摘要
空间转录组学分析成本高昂且技术要求高，限制了全转录组分析。从H&E组织学预测空间解析基因表达可弥补这一差距，但现有方法忽视组织结构且很少量化预测可信度。我们引入HierarchicalDAEW，一种双图架构。现场图上，域感知边缘加权卷积算子学习不同投影。基因级图融合先验与共表达。通过证据不确定性估计处理可靠性。在六个组织切片上，HierarchicalDAEW与真实表达相关性最强，消融实验证实相关组件的必要性，校准不确定性估计可识别低置信度预测供病理学家审查。
英文摘要
Spatial transcriptomics assays remain costly and technically demanding, restricting transcriptome-wide profiling to specialist settings and preventing routine clinical deployment. Predicting spatially resolved gene expression from H&E histology could close this gap, yet current methods largely ignore the underlying tissue architecture and rarely quantify how their predictions can be trusted. We introduce HierarchicalDAEW, a dual-graph architecture that addresses both gaps. On the spot graph, a Domain-Aware Edge-Weighted convolutional operator learns separate projections for inter-domain, intra-domain, and boundary edges derived from Leiden clustering, allowing the model to treat tissue heterogeneity as an explicit structural signal rather than an implicit one. A second gene-level graph then fuses protein-protein interaction priors from STRING-DB with tissue-specific co-expression through learned attention gating, propagating predictions from a landmark gene set to a broader gene panel. Reliability is handled through evidential uncertainty estimation, which produces far better calibrated confidence intervals than Monte Carlo dropout under identical conditions. Across six human Visium sections spanning breast, colorectal, prostate, and cerebellar tissue, and against thirteen published baselines, HierarchicalDAEW achieves the strongest correlation with ground-truth expression, with gains that hold up under multi-seed reproducibility checks and negative controls that rule out positional shortcuts. Ablations further confirm that both the domain-aware edge typing and the hierarchical depth are necessary to this improvement, and calibrated uncertainty estimates identify low-confidence predictions for pathologist review before clinical action.
Comments30 pages, 36 figures, 26 tables
