SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq

A bidirectional cross-attention and pre-trained general-purpose foundation model-based algorithm to predict super-resolved spatial gene expression by deeply integrating histology images and LR spot…

[Briefings in Bioinformatics 2025] A GCN and pathology image large model-based method to predict single-cell resolution spatial gene expression by integrating multimodal information.

[Advanced Science 2025] SpaBatch: Deep learning-based cross-slice integration and 3D spatial domain identification in spatial transcriptomics

SpaCRD: Multimodal Deep Fusion of Histology and Spatial Transcriptomics for Cancer Region Detection

[PLoS Computational Biology 2025] Domain-Adversarial Masked Autoencoder (SpaDAMA) for cell type deconvolution in spatial transcriptomics data

Globally Convergent Accelerated Block Proximal Method with Adaptive Momentum for Nonconvex Optimization

Enhancing High-density Spatial Transcriptomics from Histology Images using HisHRST

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

[Communications Biology 2025 (under Nature Portfolio)] SpaCross is a comprehensive analytical framework designed for spatial transcriptomics data, aiming to enhance the accuracy of spatial pattern …

A self-supervised learning method with Vision Transformer as backbone network for resolution enhancement in spatial transcriptomics.

SpaMCAF: Spatial Gene Expression Enhancement from Histology Images Using Multimodal Cross-Attention Fusion Network

SpaICL: Image-Guided Curriculum Strategy-Based Graph Contrastive Learning for Spatial Transcriptomics Clustering

Imputing Microbiome Data using Conditional Diffusion Generative Models

Deep Clustering Representation for Spatially Resolved Transcriptomics Data via Multi-view Variational Graph Auto-Encoders with Consensus Clustering

We propose SpatialCVGAE, which uses multiple variational graph autoencoders with different initializations to simultaneously generate corresponding low-dimensional embeddings, and then utilizes con…

SpaMGCL (Spatial Masked Graph Contrastive Learning), a novel self-supervised learning framework that integrates a masking mechanism with a contrastive learning strategy to jointly optimize gene exp…

Masked adversarial neural network for cell type deconvolution (MACD) in spatial transcriptomics data

Sparse nonnegative CP decomposition

[PLoS Computational Biology 2025] SpaMask: Dual Masking Graph Autoencoder with Contrastive Learning for Spatial Transcriptomics

Sparse Nonnegative Tucker decomposition

SpaMGAC: Masked Graph Autoencoders with Contrastive Augmentation for Spatially Transcriptomics Data Analysis