spatial transcriptomics
PulseAugur coverage of spatial transcriptomics — every cluster mentioning spatial transcriptomics across labs, papers, and developer communities, ranked by signal.
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Disentangled representations boost morpho-transcriptomic integration
Researchers have explored methods to improve the integration of spatial transcriptomics and Hematoxylin & Eosin (H&E) imaging data by disentangling shared and modality-specific variations. They compared variational auto…
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New Diffusion Model Predicts Gene Expression from Histology Images
Researchers have developed a novel method for predicting gene expression from histology images by reformulating the task as a conditional generation problem within the transcriptional program space. This approach utiliz…
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CellWorld foundation model advances spatial transcriptomics prediction
Researchers have introduced CellWorld, a novel foundation model for spatial transcriptomics that shifts its prediction target from gene expression to latent cell representations. This approach aims to improve the transf…
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New AI model predicts gene expression from histology images
Researchers have developed HierarchicalDAEW, a novel dual-graph architecture designed to predict gene expression from H&E histology images. This method addresses the limitations of current spatial transcriptomics assays…
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New COAST framework enhances gene expression prediction in spatial transcriptomics
Researchers have developed COAST, a novel framework for predicting gene expression in spatial transcriptomics using histology images. This context-aware differential learning approach leverages both local and global con…
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DriftST framework infers gene expression from histology images
Researchers have developed DriftST, a novel framework for inferring spatially resolved gene expression from H&E stained histology images. This method addresses limitations of existing approaches by enabling efficient on…
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New SN-VI Framework Enhances Latent Variable Modeling in AI
Researchers have developed Structured Nonparametric Variational Inference (SN-VI), a new framework that models complex dependencies among latent variables in posterior approximation using multivariate spline techniques.…
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New augmentation method improves spatial transcriptomics imputation
Researchers have developed SNR-ST-Mix, a novel data augmentation framework for spatial transcriptomics imputation using deep neural networks. This method addresses limitations in current augmentation strategies by ensur…
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Foundation models enable cross-modal transfer for single-cell biology
Researchers have developed a novel method for transferring information between different types of single-cell biological data. By using adversarial fine-tuning on foundation models, their approach can translate spatial …
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New method treats spatial transcriptomics as images for AI pretraining
Researchers have developed a novel method to represent spatial transcriptomics data as images for large-scale pretraining. This approach treats tissue sections as croppable image patches, allowing for a significant incr…
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New AI models integrate spatial omics data for biological insights
Researchers have developed HEIST, a hierarchical graph transformer model designed to analyze spatial transcriptomics and proteomics data. This model represents tissues as hierarchical graphs, capturing both spatial cell…
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New RankByGene method aligns gene expression with histology images
Researchers have developed a new framework called RankByGene to improve the alignment between spatial transcriptomics (ST) data and histology images. This method uses a novel ranking-based alignment loss to preserve rel…
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New GEARS Framework Reconstructs Spatial Data for Single-Cell RNA Sequencing
Researchers have developed GEARS, a novel geometry-first framework designed to reconstruct spatial information for single-cell RNA sequencing (scRNA-seq) data. Unlike previous methods that rely on fixed grids or cell-to…
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QueST method identifies cellular niches in spatial transcriptomics data
Researchers have developed QueST, a novel computational method designed to identify similar cellular niches across different spatial transcriptomics samples. This method models niches as subgraphs and utilizes contrasti…
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New benchmark SpaPath-Bench evaluates spatial understanding in pathology AI models
Researchers have introduced SpaPath-Bench, a new benchmark designed to evaluate the spatial representation capabilities of pathology foundation models (PFMs). This benchmark assesses how well PFM embeddings can distingu…
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HEXST Transformer predicts spatial gene expression from histology slides
Researchers have developed HEXST, a novel Transformer model designed to predict gene expression from histology slides. This model addresses limitations in existing methods by accounting for the hexagonal sampling patter…