FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction

Fuente: arXiv

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Bibliographic Details
Main Authors:	Si, Qi, Wang, Penglei, Wu, Yushuai, Jiao, Yifeng, Liu, Xuyang, Guo, Xin, Qi, Yuan, Cheng, Yuan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
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_version_	1866909054028218368
author	Si, Qi Wang, Penglei Wu, Yushuai Jiao, Yifeng Liu, Xuyang Guo, Xin Qi, Yuan Cheng, Yuan
author_facet	Si, Qi Wang, Penglei Wu, Yushuai Jiao, Yifeng Liu, Xuyang Guo, Xin Qi, Yuan Cheng, Yuan
contents	Predicting spatial gene expression from routine H\&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking essential biological structures like gene coordination and spatial distribution. To preserve these relationships, we introduce \textbf{FLAG}, a diffusion-based framework that redefines this task as structured distribution modeling. At the same time, we identify the critical \textbf{Gene Dimension Curse}, where joint modeling gene expression and their spatial interactions fail in high-dimensional spaces, and FLAG solves this challenge by integrating a spatial graph encoder for topological consistency and utilizing Gene Foundation Model (GFM) alignment for gene-gene fidelity in the generation process. To rigorously assess model performance, we propose a set of novel structural evaluation metrics, including Gene Structural Correlation (\textbf{GSC}) and Spatial Structural Correlation (\textbf{SSC}). Our experiments demonstrate that FLAG is highly competitive in traditional accuracy (PCC/MSE) while achieving significantly enhanced structural fidelity in capturing both gene-gene and gene-spatial relationships. The code is available at https://github.com/darkflash03/FLAG.
format	Preprint
id	arxiv_https___arxiv_org_abs_2605_18055
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction Si, Qi Wang, Penglei Wu, Yushuai Jiao, Yifeng Liu, Xuyang Guo, Xin Qi, Yuan Cheng, Yuan Machine Learning Artificial Intelligence Predicting spatial gene expression from routine H\&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking essential biological structures like gene coordination and spatial distribution. To preserve these relationships, we introduce \textbf{FLAG}, a diffusion-based framework that redefines this task as structured distribution modeling. At the same time, we identify the critical \textbf{Gene Dimension Curse}, where joint modeling gene expression and their spatial interactions fail in high-dimensional spaces, and FLAG solves this challenge by integrating a spatial graph encoder for topological consistency and utilizing Gene Foundation Model (GFM) alignment for gene-gene fidelity in the generation process. To rigorously assess model performance, we propose a set of novel structural evaluation metrics, including Gene Structural Correlation (\textbf{GSC}) and Spatial Structural Correlation (\textbf{SSC}). Our experiments demonstrate that FLAG is highly competitive in traditional accuracy (PCC/MSE) while achieving significantly enhanced structural fidelity in capturing both gene-gene and gene-spatial relationships. The code is available at https://github.com/darkflash03/FLAG.
title	FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction
topic	Machine Learning Artificial Intelligence
url	https://arxiv.org/abs/2605.18055