ArticleFrontiers in genetics2023
Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- PromptSTG: prototype-guided prompting for few-shot spatial transcriptomics annotation.Briefings in bioinformatics · 2026Article
- Histology-informed spatial domain identification through multi-view graph convolutional networks.PLoS computational biology · 2026Article
- Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SpaMWGDA: Identifying spatial domains of spatial transcriptomes using multi-view weighted fusion graph convolutional network and data augmentation.PLoS computational biology · 2025Article
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- Spatially informed graph transformers for spatially resolved transcriptomics.Communications biology · 2025Article
- Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.Bioinformatics advances · 2025Review
- Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics.Computational and structural biotechnology journal · 2024Review
- Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.Briefings in bioinformatics · 2024Article
- Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial contexts. To solve the issue, we develop a novel ensemble model, AE-GCN (
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Registered trials
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.