Article

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological Images

2024-05-07

Abstract excerpt

Spatially resolved omics technologies generating multimodal and high-throughput data lead to the urgent need for advanced analysis to allow the biological discoveries by comprehensively utilizing information from multi-omics data. The H&E image and spatial transcriptomic data indicate abundant features which are different and complementary to each other. AI algorithms can perform nonlinear analysis on these aligne...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: dd97823f-0aff-5a1b-a7bc-16e84442cf16
DOI: 10.1101/2024.05.04.592486

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological ImagesDOI 10.1101/2024.05.04.592486Read

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological ImagesSemantic proximity Read

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomicsSemantic proximity Read

H2O: A Foundation Model Bridging Histopathology to Spatial Multi-Omics ProfilingSemantic proximity Read

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survivalSemantic proximity Read

SiGra: Single-cell spatial elucidation through image-augmented graph transformerSemantic proximity Read

Position-informed contrastive learning for spatially resolved omics deciphers hierarchical tissue structure at both cellular and niche levelsSemantic proximity Read

Query-driven generative AI synthesizes multi-modal spatial omics from histologySemantic proximity Read

GatorPrism: Prototype-Conditioned Routing across Coalition Graph Experts for Spatial Multi-Omics IntegrationSemantic proximity Read

Cross-view graph neural networks for spatial domain identification by integrating gene expression, spatial locations with histological imagesSemantic proximity Read

PathCLAST: Pathway-Augmented Contrastive Learning with Attention for Spatial TranscriptomicsSemantic proximity Read

Inferring single-cell spatial gene expression with tissue morphology via explainable deep learningSemantic proximity Read

Unveiling Tissue Structure and Tumor Microenvironment from Spatial Omics by Hypergraph LearningSemantic proximity Read