arXiv Computer Vision

SAIL: A Learnable Generalisation of Spatial Autocorrelation over Graph-Structured Data

SAIL (Spatial Autocorrelation Index Learner) is a differentiable framework that extends classical spatial autocorrelation statistics, such as Moran's I and Geary's C, to high‑dimensional graph‑structured data. It learns feature projections and inter‑node neighbourhood affinities, producing interpretable local and global spatial coherence descriptors for both supervised prediction and unsupervised discovery of spatial autocorrelation modes and hotspots. Experiments on synthetic data and large‑scale biomedical graphs demonstrate that SAIL recovers traditional statistics under specific settings and can approximate Bayes‑optimal predictors for tasks driven by latent spatial autocorrelation.

arXiv Machine Learning

Sep 25

SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference

SpaFactor is a lightweight framework that predicts spatial gene expression from hematoxylin and eosin images by fusing central spot visuals with multiscale neighborhood context. It uses a residual MLP to map tissue microenvironment to low‑dimensional latent gene programs, which are decoded into coordinated multi‑gene predictions. Across five public cohorts, SpaFactor outperforms existing methods, especially for spatially variable genes, and better recovers biologically organized spatial patterns.

By Shiting Ruan, Xitong Ling, Qiming He, Ziyou Yan, Huaitian Yuan, Tian Guan, Ying Xiao, Xu Guan, Yonghong He

arXiv Machine Learning

Aug 24

Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classification

arXiv:2608.20548v1 Announce Type: cross Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplore...

By Fuad Hasan, Chul Min Yeum

arXiv Machine Learning

Jul 7

MABLE: Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning

arXiv:2607. 02990v1 Announce Type: new Abstract: We propose MABLE (Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning), a self-supervised framework for learning node and graph embeddings from large, heterogeneous graphs, demonstrated here on geospatial mineral-exploration data.

By Yaniv Shulman, Shaghayegh Akbarpour, Jack B. Muir

rag safety

arXiv Machine Learning

Jun 5

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data

arXiv:2506. 11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level.

By Hiren Madhu, Jo\~ao Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying

llms rag benchmarks

arXiv AI

Oct 21

Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics

arXiv:2610.28579v1 Announce Type: cross Abstract: Cellular states are shaped by both intrinsic gene expression programs and the spatial microenvironment in which cells reside. Spatial transcriptomics...

By Azim Dehghani Amirabad, Ali Khodabandeh Yalabadi, Artem Moskalev, Yun-Ching Chen, Mehdi Pirooznia, Scott Oloff, Rui Liao

safety

arXiv Machine Learning

Aug 26

BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval

arXiv:2608.24823v1 Announce Type: new Abstract: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences....

By Seungik Cho, Betul Orcan-Ekmekci

rag multimodal safety

arXiv Computer Vision

Oct 27

Spatially-Aware Self-Organizing Graph Autoencoders via Localized Hebbian Learning

arXiv:2610.34346v1 Announce Type: new Abstract: Traditional autoencoders rely on static network architectures to learn latent representations. Recently we suggested a dynamic architecture inspired by...

By Robin Ko\c{c}, Hazal Mo\u{g}ultay \"Ozcan, Fato\c{s} T. Yarman Vural

arXiv Computer Vision

Sep 16

Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics

arXiv:2609.16207v1 Announce Type: new Abstract: Spatial Transcriptomics (ST) has transformed biomedical research by enabling the spatial mapping of gene expression across tissue sections. However, hi...

By Daniela Vega, Paula C\'ardenas, Hannah Ceballos, Leonardo Manrique, Pablo Arbela\'ez

computer-vision

arXiv Machine Learning

Jul 16

CDS: Counterfactual Directionality Score for Structured Interventions in Spatial Graphs

arXiv:2607. 13508v1 Announce Type: new Abstract: Quantifying directional influence between node populations is a fundamental problem in graph-based modeling, particularly in spatial biological systems where cell-cell interactions shape functional outcomes.

By Humaira Anzum, Md Ishtyaq Mahmud, Jagan Mohan Reddy Dwarampudi, Tania Banerjee

arXiv Machine Learning

Aug 21

SAGE-XGBoost: Spatially Augmented Graph Embeddings--Machine Learning Framework for Natural Hazards Susceptibility Mapping under Data Scarcity

arXiv:2608. 19672v1 Announce Type: new Abstract: Natural hazard susceptibility mapping is often constrained by limited labeled data, reducing the generalizability of conventional machine learning and limiting the applicability of complex deep learning models.

By Mohammad H. Vahidnia, Ali Pourkarimi

rag

arXiv AI

Jun 9

SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network

arXiv:2606. 08712v1 Announce Type: cross Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context.

By Hongyi Yu, Yaoyu Fang, Jiahe Qian, Xinkun Wang, Lee A. Cooper, Bo Zhou

arXiv Machine Learning

Jul 13

COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics

arXiv:2607. 09166v1 Announce Type: new Abstract: Spatial transcriptomics enables profiling of spatial gene expression but is limited by high cost and low throughput, motivating prediction from H&E histopathology images.

By Keunho Byeon, Sunhong Park, Jeewoo Lim, Jin Tae Kwak

llms efficiency

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