Aug 2026· Bioinformatics Advances· Vol 6· 0 citations· 50 references
Medicine
TL;DR
AINR is an end-to-end deep learning framework that models spatial transcriptomics data as a geometrically constrained continuous biological field, using a periodic sine activation function to map spatial coordinates directly to gene expression while preserving spatial smoothness without explicit adjacency matrices.
Abstract
Abstract Motivation Spatial transcriptomics measures gene expression together with spatial locations, but its data are noisy and sparse, and existing graph-based methods are complex and hard to scale. Results We present AINR, an end-to-end deep learning framework that models spatial transcriptomics data as a geometrically constrained continuous biological field. AINR combines implicit neural representations with a spatially-aware attention mechanism and a total variation regularization term, using a periodic sine activation function to map spatial coordinates directly to gene expression while preserving spatial smoothness without explicit adjacency matrices. Across six diverse datasets, AINR consistently outperforms existing methods in spatial domain identification and remains robust even under extreme data sparsity. Availability and implementation The code for AINR is available at https://github.com/XGD1122/AINR
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