# Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features

arXiv: https://arxiv.org/abs/2403.07592 · HTML: https://mlsanity.com/paper/2403.07592/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Conditional Positional Encodings for Vision Transformers](https://mlsanity.com/paper/2102.10882/) (2021)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Patches Are All You Need?](https://mlsanity.com/paper/2201.09792/) (2022)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning](https://mlsanity.com/paper/2306.01859/) (2023)
- [Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction](https://mlsanity.com/paper/2210.16721/) (2022)
- [S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation](https://mlsanity.com/paper/2011.14785/) (2020)

## Built on by (most cited first, 30 of 52)

- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images](https://mlsanity.com/paper/2501.15598/) (2025)
- [Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching](https://mlsanity.com/paper/2506.05361/) (2025)
- [MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images](https://mlsanity.com/paper/2412.02601/) (2024)
- [M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Images](https://mlsanity.com/paper/2409.15092/) (2024)
- [ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics](https://mlsanity.com/paper/2412.03026/) (2024)
- [MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics](https://mlsanity.com/paper/2502.21011/) (2025)
- [Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning](https://mlsanity.com/paper/2506.23827/) (2025)
- [Img2ST-Net: Efficient High-Resolution Spatial Omics Prediction from Whole Slide Histology Images via Fully Convolutional Image-to-Image Learning](https://mlsanity.com/paper/2508.14393/) (2025)
- [HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction](https://mlsanity.com/paper/2601.21560/) (2026)
- [RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency](https://mlsanity.com/paper/2411.15076/) (2024)
- [PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction](https://mlsanity.com/paper/2503.16816/) (2025)
- [GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction](https://mlsanity.com/paper/2510.04315/) (2025)
- [Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics](https://mlsanity.com/paper/2512.06612/) (2025)
- [Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images](https://mlsanity.com/paper/2603.19766/) (2026)
- [SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics](https://mlsanity.com/paper/2412.01124/) (2024)
- [Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics](https://mlsanity.com/paper/2511.17685/) (2025)
- [Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images](https://mlsanity.com/paper/2603.18461/) (2026)
- [Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder](https://mlsanity.com/paper/2503.07173/) (2025)
- [Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images](https://mlsanity.com/paper/2507.14670/) (2025)
- [MVHybrid: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models](https://mlsanity.com/paper/2508.00383/) (2025)
- [MMAP: A Multi-Magnification and Prototype-Aware Architecture for Predicting Spatial Gene Expression](https://mlsanity.com/paper/2510.11344/) (2025)
- [HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology](https://mlsanity.com/paper/2511.12969/) (2025)
- [SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome](https://mlsanity.com/paper/2511.15464/) (2025)
- [Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection](https://mlsanity.com/paper/2511.18336/) (2025)
- [SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning](https://mlsanity.com/paper/2512.13635/) (2025)
- [FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics](https://mlsanity.com/paper/2603.25247/) (2026)
- [What makes for good morphology representations for spatial omics?](https://mlsanity.com/paper/2407.20660/) (2024)
- [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)

## Abstract (arXiv)

Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However, the high costs and methodological limitations of ST necessitate a more robust predictive model. In response, this paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing cellular morphology at individual spots, the local context around these spots, and the global tissue organization. By integrating these features through an effective fusion strategy, TRIPLEX achieves accurate gene expression prediction. Our comprehensive benchmark study, conducted on three public ST datasets and supplemented with Visium data from 10X Genomics, demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC). The model's predictions align closely with ground truth gene expression profiles and tumor annotations, underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.