ArticleGenome research2025
A novel multislice framework for precision 3D spatial domain reconstruction and disease pathology analysis.
Article in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Integrated Transcriptomic Analyses Identify Four Prognosis-Associated Genes in Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.BMC biology · 2026Article
- KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.BMC biology · 2026Article
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.BMC biology · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Emerging pathological mechanisms of Alzheimer's disease pathogenesis: from neuroimmune interactions to intercellular communication.Frontiers in aging neuroscience · 2026Review
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- [Management and consideration of positive surgical margins in locally advanced oropharyngeal squamous cell carcinoma treated with oral robotic surgery].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025Article
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Authors and funding
4 authors.
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Abstract
The development of spatial transcriptomics (ST) technologies has revolutionized the way we map the complex organization and functions of tissues. These technologies offer valuable insights into the organization and function of complex biological systems. However, existing methods often focus too narrowly on single modalities or resolutions, thereby hindering the comprehensive capture of multilayered biological heterogeneity. Here, STMSC is proposed as a multislice joint analysis framework featuring a precorrection mechanism that enables the precise identification of complex spatial domains, advancing disease pathology insights. STMSC assumes that precise three-dimensional (3D) reconstruction is essential for an in-depth investigation of tissue components and mechanisms. Incorporating hematoxylin and eosin (H&E) imaging data, STMSC enhances slice alignment accuracy in 3D reconstruction. By deconstructing microenvironments, it reconstructs fine-grained cellular landscapes and emphasizes collective cellular behavior in defining spatial domains. Its graph attention autoencoder with precorrection balances biological information at different levels, improving the accuracy of ST analyses. By analyzing consecutive tissue slices and pathological data sets, STMSC accurately reconstructs 3D structures and provides deeper insights into complex cancer environments. Specifically, STMSC captures intra- and interstage heterogeneity in cancer development, offering novel insights into the complexity of pathological tissue structures.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.