# Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction

arXiv: https://arxiv.org/abs/2401.14772 · HTML: https://mlsanity.com/paper/2401.14772/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction](https://mlsanity.com/paper/2210.16721/) (2022)
- [A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning](https://mlsanity.com/paper/1706.08653/) (2017)
- [SEPAL: Spatial Gene Expression Prediction from Local Graphs](https://mlsanity.com/paper/2309.01036/) (2023)

## Built on by (most cited first, 6 of 6)

- [Enhancing Features in Long-tailed Data Using Large Vision Model](https://mlsanity.com/paper/2504.10852/) (2025)
- [Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics](https://mlsanity.com/paper/2511.17685/) (2025)
- [CLIP-IT: CLIP-based Pairing for Histology Images Classification](https://mlsanity.com/paper/2504.16181/) (2025)
- [Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images](https://mlsanity.com/paper/2507.14670/) (2025)
- [From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images](https://mlsanity.com/paper/2503.01347/) (2025)
- [GATE-ST: Gene-Aware Text-image Encoder for Spatial Transcriptomics](https://mlsanity.com/paper/2609.38690/) (2026)

## Abstract (arXiv)

Spatial transcriptomics (ST) captures gene expression within distinct regions (i.e., windows) of a tissue slide. Traditional supervised learning frameworks applied to model ST are constrained to predicting expression from slide image windows for gene types seen during training, failing to generalize to unseen gene types. To overcome this limitation, we propose a semantic guided network (SGN), a pioneering zero-shot framework for predicting gene expression from slide image windows. Considering a gene type can be described by functionality and phenotype, we dynamically embed a gene type to a vector per its functionality and phenotype, and employ this vector to project slide image windows to gene expression in feature space, unleashing zero-shot expression prediction for unseen gene types. The gene type functionality and phenotype are queried with a carefully designed prompt from a pre-trained large language model (LLM). On standard benchmark datasets, we demonstrate competitive zero-shot performance compared to past state-of-the-art supervised learning approaches.