Minxing Pang
@minxingpang.bsky.social
Applied Math PhD student @upenn.edu
3/3 By leveraging generative AI, our framework significantly improves downstream tasks compared to traditional methods, including better cell type annotation and patient survival prediction. 📉✅
Authors: Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, and Kai Tan.
#GenerativeAI #SpatialOmics #Pathology
Authors: Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, and Kai Tan.
#GenerativeAI #SpatialOmics #Pathology
December 15, 2025 at 7:35 PM
3/3 By leveraging generative AI, our framework significantly improves downstream tasks compared to traditional methods, including better cell type annotation and patient survival prediction. 📉✅
Authors: Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, and Kai Tan.
#GenerativeAI #SpatialOmics #Pathology
Authors: Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, and Kai Tan.
#GenerativeAI #SpatialOmics #Pathology
2/3 To power this, we created PRISM-12M: a massive dataset of 12M+ patches across 14 tissue types. 📂
TissueCraftAI uses this to generate multi-modal spatial transcriptomics & proteomics maps from simple natural language prompts, covering both human and mouse tissues. 🐭🧍♂️
TissueCraftAI uses this to generate multi-modal spatial transcriptomics & proteomics maps from simple natural language prompts, covering both human and mouse tissues. 🐭🧍♂️
December 15, 2025 at 7:34 PM
2/3 To power this, we created PRISM-12M: a massive dataset of 12M+ patches across 14 tissue types. 📂
TissueCraftAI uses this to generate multi-modal spatial transcriptomics & proteomics maps from simple natural language prompts, covering both human and mouse tissues. 🐭🧍♂️
TissueCraftAI uses this to generate multi-modal spatial transcriptomics & proteomics maps from simple natural language prompts, covering both human and mouse tissues. 🐭🧍♂️
1/3 Excited to share our latest preprint: TissueCraftAI! 🚀
It's the FIRST 1B-parameter spatial omics model built on ControlNet & Stable Diffusion. 🧬🤖
Moving beyond CNNs, it enables query-driven prediction of molecular maps directly from histology.
🔗 www.biorxiv.org/content/10.6...
It's the FIRST 1B-parameter spatial omics model built on ControlNet & Stable Diffusion. 🧬🤖
Moving beyond CNNs, it enables query-driven prediction of molecular maps directly from histology.
🔗 www.biorxiv.org/content/10.6...
Query-driven generative AI synthesizes multi-modal spatial omics from histology
Spatial omics technologies offer unprecedented insights into the cellular organization of tissues; however, they are not yet scalable for routine clinical use. In contrast, images of histological stai...
www.biorxiv.org
December 15, 2025 at 7:33 PM
1/3 Excited to share our latest preprint: TissueCraftAI! 🚀
It's the FIRST 1B-parameter spatial omics model built on ControlNet & Stable Diffusion. 🧬🤖
Moving beyond CNNs, it enables query-driven prediction of molecular maps directly from histology.
🔗 www.biorxiv.org/content/10.6...
It's the FIRST 1B-parameter spatial omics model built on ControlNet & Stable Diffusion. 🧬🤖
Moving beyond CNNs, it enables query-driven prediction of molecular maps directly from histology.
🔗 www.biorxiv.org/content/10.6...