# MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images

arXiv: https://arxiv.org/abs/2412.02601 · HTML: https://mlsanity.com/paper/2412.02601/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Patches Are All You Need?](https://mlsanity.com/paper/2201.09792/) (2022)
- [Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://mlsanity.com/paper/2306.07831/) (2023)
- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning](https://mlsanity.com/paper/2306.01859/) (2023)
- [Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features](https://mlsanity.com/paper/2403.07592/) (2024)
- [Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction](https://mlsanity.com/paper/2210.16721/) (2022)

## Built on by (most cited first, 17 of 17)

- [Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images](https://mlsanity.com/paper/2603.19766/) (2026)
- [Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images](https://mlsanity.com/paper/2603.18461/) (2026)
- [Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images](https://mlsanity.com/paper/2507.14670/) (2025)
- [SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome](https://mlsanity.com/paper/2511.15464/) (2025)
- [FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics](https://mlsanity.com/paper/2603.25247/) (2026)
- [From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images](https://mlsanity.com/paper/2503.01347/) (2025)
- [Learning from Gene Names, Expression Values and Images: Contrastive Masked Text-Image Pretraining for Spatial Transcriptomics Representation Learning](https://mlsanity.com/paper/2509.16892/) (2025)
- [DGA-Net: Enhancing SAM with Depth Prompting and Graph-Anchor Guidance for Camouflaged Object Detection](https://mlsanity.com/paper/2601.02831/) (2026)
- [AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image](https://mlsanity.com/paper/2602.20187/) (2026)
- [Cross-Domain Molecular Relational Learning: Leveraging Chemical Structure-Activity Analysis](https://mlsanity.com/paper/2605.16799/) (2026)
- [FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction](https://mlsanity.com/paper/2605.18055/) (2026)
- [GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics](https://mlsanity.com/paper/2606.02424/) (2026)
- [Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics](https://mlsanity.com/paper/2606.21156/) (2026)
- [HierarchicalDAEW: Domain-Aware Edge-Weighted Graph Convolution with Evidential Uncertainty for Multi-Section Spatial Gene Expression Prediction from H&E Histology](https://mlsanity.com/paper/2607.20896/) (2026)
- [Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics](https://mlsanity.com/paper/2609.16207/) (2026)
- [Preserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression Prediction](https://mlsanity.com/paper/2609.33928/) (2026)
- [GATE-ST: Gene-Aware Text-image Encoder for Spatial Transcriptomics](https://mlsanity.com/paper/2609.38690/) (2026)

## Abstract (arXiv)

Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods fail to fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques across multiple metrics.