CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models
Haiping Liu, Qian Zhao, Lijing Lin, Jingyuan Sun, Hongpeng Zhou
cs.AI
Submitted: 2026-08-07
Updated: 2026-08-10
Code: https://github.com/UoM-HealthAI/CellWorld
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
- VL-JEPA: Joint Embedding Predictive Architecture for Vision-language
- Cell-JEPA: Latent Representation Learning for Single-Cell Transcriptomics
- When Does LeJEPA Learn a World Model?
- HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
- V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
- Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
- SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
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