Minsheng Hao

This paper introduces CellTempo, a framework that forecasts long-range cell state trajectories from static transcriptomic data, demonstrating its ability to predict lineage progression and distinguish perturbation responses from long-term fate changes.

scDiffusion-X generates high-fidelity multi-omics data and enables modality translation with uncertainty quantification and gene regulatory network inference.

PaSCient learns patient-level disease representations from scRNA-seq data, enabling disease classification and downstream analyses like dimensionality reduction and patient subgroup discovery.

This paper introduces scGeneLens, a framework to analyze how different AI models perceive cells, revealing that scFoundation focuses on cell-type markers while scGPT emphasizes shared pathways.

BrainBridge integrates single-cell transcriptomic data with phenotypes to model brain biology, prioritizing genes and cell types linked to Alzheimer's, aging, and sex differences.

PerTurboAgent predicts gene panels for sequential Perturb-Seq experiments using an LLM-based agent, outperforming existing strategies in identifying phenotypically impactful genes.

GeST is a generative transformer model pretrained on spatial transcriptomics data to learn spatial context, showing improved generation and zero-shot spatial niche embedding extraction.

SpatialAgent integrates large language models with dynamic tool retrieval, multimodal interpretation, and claim verification to automate end-to-end spatial biology workflows from gene-panel design to tissue annotation and hypothesis generation.

PathOmCLIP infers spatial gene expression from histology images by combining pathology and single-cell RNA-seq foundation models, achieving better gene expression prediction than existing methods.

This paper introduces scFoundation, a large-scale pretrained model for single-cell transcriptomics, achieving state-of-the-art results in multiple downstream tasks.

The paper introduces scMulan, a large AI model trained to generate cell transcriptomics data with specific attributes, showing it can generate pseudo data, predict missing attributes, and identify driving regulators.

scDirect identifies key transcription factors driving cell state transitions using single-cell multi-omics data, showing robust performance in benchmarking and real-world applications.

This paper introduces HoloNet, a method to decode functional cell-cell communication events from spatial transcriptomic data by integrating ligand-receptor pairs, cell-type spatial distribution, and downstream gene expression.

xTrimoGene is a scalable transformer model for scRNA-seq data that reduces computational demands while maintaining accuracy, achieving state-of-the-art results in downstream tasks.

STEM maps single-cell and spatial transcriptomics data using transfer learning to create spatially aware embeddings and predict pseudo-spatial adjacencies.

The paper introduces hECA, a cell-centric human Ensemble Cell Atlas, which integrates scRNA-seq data from multiple studies to enable targeted data retrieval, multi-view presentations, and customizable reference creation for cell type annotations.

SOMDE identifies spatially variable genes in large-scale spatial transcriptomic data efficiently using self-organizing maps and Gaussian Processes.