mlsanityData to 14 Oct 2026

A Multimodal Foundation Model of Spatial Transcriptomics and Histology for Biological Discovery and Clinical Prediction

arXiv 2604.03630 ↗First posted 4 Apr 2026cs.AI q-bio.QMcited by 3

Not on the map. Why?

Abstract (arXiv)

Spatial transcriptomics (ST) enables gene expression mapping within anatomical context but remains costly and low-throughput. Hematoxylin and eosin (H&E) staining offers rich morphology yet lacks molecular resolution. We present ours (Spatial Transcriptomics and histOlogy Representation Model), a foundation model trained on 1.2 million spatially resolved transcriptomic profiles with matched histology across 18 organs. Using a hierarchical architecture integrating morphological features, gene expression, and spatial context, STORM bridges imaging and omics through robust molecular--morphological representations. STORM enhances spatial domain discovery, producing biologically coherent tissue maps, and outperforms existing methods in predicting spatial gene expression from H&E images across 11 tumor types. The model is platform-agnostic, performing consistently across Visium, Xenium, Visium HD, and CosMx. Applied to 23 independent cohorts comprising 7,245 patients, STORM significantly improves immunotherapy response prediction and prognostication over established biomarkers, providing a scalable framework for spatially informed discovery and clinical precision medicine.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 7 of its 75 references that are in the map. First posted on arXiv 4 Apr 2026.

Builds on

7 of 75 references.

References · 7

  1. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  2. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  3. 2021Perceiver IO: A General Architecture for Structured Inputs & Outputscited by 629
  4. 2023Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learningcited by 79
  5. 2022Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Predictioncited by 39
  6. 2025Distilling foundation models for robust and efficient models in digital pathologycited by 36
  7. 2024Multimodal contrastive learning for spatial gene expression prediction using histology imagescited by 30

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Stainingcited by 2
  2. 2026VOICE: A Vision-Omics Foundation Model Integrating Direct and Retrieval-Based Prediction of In-situ Single-Cell Gene Expressioncited by 1
  3. 2026HierarchicalDAEW: Domain-Aware Edge-Weighted Graph Convolution with Evidential Uncertainty for Multi-Section Spatial Gene Expression Prediction from H&E Histologycited by 0