arXiv AI
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Oct 13
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
The paper investigates how arranging single‑cell RNA sequencing data into images (Gene Expression‑to‑Image, GE2I) affects learning with convolutional neural networks. By keeping the network and training procedure constant, the authors uncover a trade‑off between spatial smoothness and gene‑level fidelity: structured mappings yield smooth, low‑frequency patterns that accelerate early optimization, while assigning multiple genes to the same pixel can obscure expression differences and hurt predictive performance. They show that one‑to‑one gene‑to‑pixel assignments improve predictions across various visual backbones, and that the benefits of pretraining and augmentation depend on both the backbone and the mapping strategy, offering practical guidance for pixel allocation and transfer learning in gene expression analysis.
arXiv Machine Learning
Jul 17
A vision foundation model for single-cell biology via spatial gene cartography
arXiv:2607. 14163v1 Announce Type: cross Abstract: Most single-cell foundation models are adapted from language models, representing each cell as a sequence of gene tokens.
arXiv AI
Oct 1
GATE-ST: Gene-Aware Text-image Encoder for Spatial Transcriptomics
GATE-ST is a gene-aware text-image encoder that enhances spatial transcriptomics predictions by integrating gene descriptions into image-based models. The method encodes gene summaries with a text encoder and fuses these embeddings with image features via cross‑attention, aligning them with morphological cues. Benchmarks show GATE‑ST outperforms random gene embeddings and other image‑text fusion architectures, indicating its potential to improve accuracy while reducing time and cost in spatial gene expression analysis.
arXiv AI
Jun 4
Spatial Transcriptomics as Images for Large-Scale Pretraining
arXiv:2603. 13432v4 Announce Type: replace-cross Abstract: Spatial Transcriptomics (ST) profiles thousands of gene expression values at discrete spots with precise coordinates on tissue sections, preserving spatial context essential for clinical and pathological studies.
arXiv AI
Jun 17
SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
arXiv AI
Aug 18
Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics
arXiv:2608. 14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST).