ArticleBriefings in bioinformatics2024
Gene expression prediction from histology images via hypergraph neural networks.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- DeepPathway: predicting pathway expression from histopathology images.Bioinformatics (Oxford, England) · 2026Article
- Review
- From histology to spatial transcriptomics: establishing a lightweight single-patch baseline.BMC bioinformatics · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- GR2ST: spatial transcriptomics prediction based on graph-enhanced multimodal contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- GR2ST: spatial transcriptomics prediction based on graph-enhanced multimodal contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Predicting protein cascade expression from H&E images.PLoS computational biology · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology.Frontiers in oncology · 2026Article
- The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities.Frontiers in public health · 2026Review
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- Reg2ST: recognizing potential patterns from gene expression for spatial transcriptomics prediction.Briefings in bioinformatics · 2025Article
- Hybrid attention transformer integrated YOLOV8 for fruit ripeness detection.Scientific reports · 2025Article
- HECLIP: histology-enhanced contrastive learning for imputation of transcriptomics profiles.Bioinformatics (Oxford, England) · 2025Article
- AI-Driven Transcriptome Prediction in Human Pathology: From Molecular Insights to Clinical Applications.Biology · 2025Review
- ICMC: An Interpretable Cross-domain Multi-modal Classification model for grading teaching plan.PloS one · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.
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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.