graph neural network

Graph neural network approach for inferring neuronal connectivity

This paper introduces CircuitGNN, a graph neural network that infers neural connectivity from population activity, showing high accuracy and robustness across different circuit sizes and recording conditions.

Predicting clonal mutations from tumour spatial transcriptomics data using a graph convolutional neural network

This paper introduces SPOT-Mut, a graph convolutional neural network that predicts clonal mutations from spatial transcriptomics data, achieving high precision and recall for key driver genes.

Overcoming segmentation bottlenecks in brain tissue for spatial proteomic inference at population scale

The paper introduces SPInfer, a workflow for spatial proteomic inference that improves cell segmentation in human brain tissue, enabling better biological discovery at population scale.

Phenotype-cloud reorganization shapes cancer progression and therapeutic adaptation

This study identifies phenotype-cloud transitions as an organizational feature of cancer progression and therapeutic adaptation, showing how tumours reorganize phenotypic states during progression and treatment.

geoGraph: Connectivity modelling across geographic space with hexagonal discrete global grids

geoGraph models organism movement and connectivity across geographic space using hexagonal grids, improving accuracy over rectangular grids by avoiding distortion.

Histology-aware Inference of Cell-cell Communication in Spatial Transcriptomics

This paper introduces HARMONIC, a framework that improves cell-cell communication inference in spatial transcriptomics by incorporating histology data, showing it better captures anatomical context.

GRASS-MIL: graph-based representation and discovery of phenotype-associated spatial structures with multiple instance learning

GRASS-MIL predicts phenotypes from spatial single-cell data and decomposes predictions into specific cellular neighborhoods, showing improved performance over composition-based methods.

ST-DISTAL: Dual-Branch Graph Convolution with Distributional Alignment for Cell-Type Deconvolution

ST-DISTAL improves cell-type deconvolution in spatial transcriptomics by integrating spatial and molecular information through a dual-branch graph convolutional framework.

SpatialTRACE predicts anatomical axes and regions in spatial transcriptomics and microscopy

SpatialTRACE extends anatomical annotations from a few structures to tissue-wide maps using graph and image-based models in spatial transcriptomics and microscopy.

StressNET: an adaptable deep-learning model for mechanical stress inference in tissues

StressNET infers intercellular mechanical stresses from tissue geometry using a Graph Neural Network, achieving state-of-the-art performance in zebrafish and Xenopus embryos.

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

This paper introduces CIFM, a generative virtual tissue model that simulates transcriptional dynamics under therapeutic conditions to design immunotherapy strategies for cancer and autoimmune diseases.

Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization

SQUINT learns discrete cell and niche codes from spatial transcriptomics data using residual vector quantization, outperforming baselines in identification and cross-section integration.

Mapping disease traits onto spatiotemporal domain landscapes through scalable multi-sample integration of spatial transcriptomics

STUltra integrates spatial transcriptomics data to map disease traits onto spatiotemporal tissue landscapes, outperforming existing methods in scalability and speed.

Multi-modal Graph Integration for Biologically Interpretable Domain Identification Using Spatial Transcriptomics

This study introduces BIGraph-ST, a method that integrates pathway activity and histological features to improve spatial domain identification in spatial transcriptomics, showing better performance in cancer tissues.

SpatialJEPA: JEPA-inspired graph-context distillation for spatially aware multiomics integration

SpatialJEPA transfers spatial context from spatial multiomics data to dissociated non-spatial data through graph-context distillation, improving alignment and recovery of spatially organized programs in mouse brain multiomics.

Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer

The paper introduces STORM, a method to identify recurrent spatial motifs in colorectal cancer without cell-type labels, finding a fibroblast barrier linked to immune exclusion and prognosis.

Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics

This paper introduces st-Xprop, a graph learning method for spatial domain detection in multi-modal spatial transcriptomics, improving clustering accuracy and biological relevance.

SPARC Reveals Distinct Osteoclastogenesis-Associated Malignant States in Metastatic Osteosarcoma

SPARC improves trajectory inference by jointly modeling state progression and transitions between conditions, revealing osteoclastogenesis-associated malignant states in osteosarcoma metastasis.

Connectome-scale self-supervised representation learning reveals neuronal organization beyond canonical labels

This paper develops a self-supervised framework for learning structure-connectivity representations in dense connectomes, revealing neuronal organization beyond canonical labels.

SpaGRD deciphers signaling architectures in spatial transcriptomics using graph reaction-diffusion systems

SpaGRD models cell-cell communication using graph reaction-diffusion systems, revealing spatially resolved signaling patterns in spatial transcriptomics data.

Deciphering shared and divergent tissue architectures from cross-species spatial transcriptomics

This paper introduces STACAME, a framework for analyzing cross-species spatial transcriptomics data to identify shared and divergent tissue architectures across species.

Deciphering cross-omics complexity of tissues via diagonal integration of unpaired spatial multi-omics data

This paper introduces STAMO, a spatially aware integration method for unpaired spatial multi-omics data, showing improved alignment and domain detection compared to existing methods.

Chronic Stress Exacerbates Long-term Microvascular Network Dysfunction Following Brain Trauma

This study shows that chronic stress worsens microvascular network dysfunction after brain trauma, reducing capillary coordination and vascular reactivity.

Cell type-centric interaction networks define spatial architecture of intrahepatic cholangiocarcinoma

This study defines the spatial architecture of intrahepatic cholangiocarcinoma using single-cell spatial proteomics and a graph-based deep learning framework to identify multicellular spatial patterns and their association with patient outcomes.

HyperNiche: Learning Heterophilic Cellular Niches with Hypergraph Neural Networks

HyperNiche improves clustering and biological interpretability of spatial transcriptomics data by modeling higher-order cellular niches with hypergraph neural networks.

AI-Guided Structure-Aware Modeling and Thermal Proteomics Reveal Direct Demethylzeylasteral-ACLY Interaction

The paper uses AI and thermal proteomics to identify a natural product's direct target and shows its therapeutic effect in a psoriasis model.

GraphTME: a framework for predicting response to immune checkpoint inhibitors by interpreting cell-cell interactions in the tumour microenvironment using spatial transcriptomics of tumour tissue

GraphTME predicts immune checkpoint inhibitor response by analyzing spatial cell-cell interactions in tumor microenvironments using spatial transcriptomics data.

Heterogeneity-driven adaptive scale graph learning for subcellular spatial transcriptomics

This paper introduces HAST, a framework for spatial transcriptomics that adapts graph learning scales to tissue heterogeneity, improving spatial structure identification and tumor region characterization.

DGAT: A Dual-Graph Attention Network for Inferring Spatial Protein Landscapes from Transcriptomics

DGAT imputes spatial protein expression from transcriptomics-only data, revealing cell states and tissue organization not visible from RNA alone.

pyTrance finds co-localizing RNAs in subcellular spatial transcriptomics data

pyTrance identifies co-localizing RNAs in spatial transcriptomics data with higher accuracy than existing methods, revealing biologically relevant RNA co-localization patterns in mouse brain tissue.