FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics

• URL: http://arxiv.org/abs/2603.25247v1
• Date: Thu, 26 Mar 2026 09:51:06 GMT
• Title: FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics
• Abstract summary: FEAST (Fully connected Expressive Attention for Spatial Transcriptomics) is an attention-based framework that models the tissue as a fully connected graph.<n>We show that FEAST surpasses state-of-the-art methods in gene expression prediction while providing biologically plausible attention maps.
• Score: 10.003518151876328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial Transcriptomics (ST) provides spatially-resolved gene expression, offering crucial insights into tissue architecture and complex diseases. However, its prohibitive cost limits widespread adoption, leading to significant attention on inferring spatial gene expression from readily available whole slide images. While graph neural networks have been proposed to model interactions between tissue regions, their reliance on pre-defined sparse graphs prevents them from considering potentially interacting spot pairs, resulting in a structural limitation in capturing complex biological relationships. To address this, we propose FEAST (Fully connected Expressive Attention for Spatial Transcriptomics), an attention-based framework that models the tissue as a fully connected graph, enabling the consideration of all pairwise interactions. To better reflect biological interactions, we introduce negative-aware attention, which models both excitatory and inhibitory interactions, capturing essential negative relationships that standard attention often overlooks. Furthermore, to mitigate the information loss from truncated or ignored context in standard spot image extraction, we introduce an off-grid sampling strategy that gathers additional images from intermediate regions, allowing the model to capture a richer morphological context. Experiments on public ST datasets show that FEAST surpasses state-of-the-art methods in gene expression prediction while providing biologically plausible attention maps that clarify positive and negative interactions. Our code is available at https://github.com/starforTJ/ FEAST.

Related papers

• Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics [0.0]
  We propose a hyperbolic contrastive learning model that captures the intrinsic hierarchical relationships within spatial Transcriptomics data.<n>By leveraging hyperbolic geometry and a gene-to-image entailment loss, HyCLoST learns structured, biologically grounded representations that improve gene expression prediction accuracy.
  arXiv  Detail & Related papers  (2026-09-14T18:39:51Z)
• Cross-Subject Semantic Decoding with Shared-Space Alignment for Generalized Neural Representation Learning [80.18561652862725]
  Generalizing across subjects remains challenging in invasive neural recordings.<n>We propose a cross-subject semantic decoding framework that aligns neural responses to speech perception from multiple subjects into a shared latent space.
  arXiv  Detail & Related papers  (2026-07-03T03:59:16Z)
• Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference [7.79162872496708]
  SpaHGC is a graph-based model that captures both intra-slice and inter-slice spot-spot relationships from histology images.<n>It integrates local spatial context within the target slide and cross-slide similarities computed from image embeddings extracted by a pathology foundation model.<n>SpaHGC significantly outperforms the existing nine state-of-the-art methods across all evaluation metrics.
  arXiv  Detail & Related papers  (2026-03-24T05:49:00Z)
• Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning [12.35331063443348]
  NH2ST integrates spatial context and both pathology and gene modalities for gene expression prediction.<n>Our model consistently outperforms existing methods, achieving over 20% in PCC metrics.
  arXiv  Detail & Related papers  (2025-06-30T13:18:39Z)
• Improving Medical Visual Representation Learning with Pathological-level Cross-Modal Alignment and Correlation Exploration [21.260659596426184]
  We propose a novel pathological-level cross-modal alignment (PCMA) approach to maximize the consistency of pathology observations from both images and reports.<n>The PCMA module operates independently of any external disease annotations, enhancing the generalizability and robustness of our methods.<n> Experimental results demonstrate that our proposed framework achieves new state-of-the-art performance on multiple downstream tasks.
  arXiv  Detail & Related papers  (2025-06-12T11:01:57Z)
• Message Intercommunication for Inductive Relation Reasoning [49.731293143079455]
  We develop a novel inductive relation reasoning model called MINES. We introduce a Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph. Our experiments show that MINES outperforms existing state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-23T13:51:46Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity [52.67037774136973]
  Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. We propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool)
  arXiv  Detail & Related papers  (2021-07-21T03:34:09Z)
• DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation [11.120241862037911]
  Heterogeneous information network has been widely used to alleviate sparsity and cold start problems in recommender systems. We propose a novel disentangled heterogeneous graph attention network DisenHAN for top-$N$ recommendation.
  arXiv  Detail & Related papers  (2021-06-21T06:26:10Z)
• Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [54.93890176891602]
  We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
  arXiv  Detail & Related papers  (2020-12-17T17:44:01Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.