Haiyang Bian

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.

This paper introduces CellTok, a method to convert single-cell transcriptomes into tokens for LLMs, enabling joint processing of cellular data and text.

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.

This study benchmarks 10 in silico gene perturbation methods across four scenarios, identifying strengths and limitations in predicting perturbation effects and zero-shot transfer.

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.

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.