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

• URL: http://arxiv.org/abs/2605.18055v1
• Date: Mon, 18 May 2026 08:43:18 GMT
• Title: FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction
• Abstract summary: Current models overlook essential biological structures like gene coordination and spatial distribution.<n>We introduce textbfFLAG, a diffusion-based framework that redefines this task as structured distribution modeling.<n>Our experiments demonstrate that FLAG is highly competitive in traditional accuracy.
• Score: 21.300129464820984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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