Chen Li

Forecasting cell state futures from static snapshots

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.

OmicOS: A Comprehensive Omics Ecosystem Infrastructure and Agent System for the AI Era

OmicOS provides an agent-operable omics ecosystem that enables AI systems to execute and verify analyses using community methods, achieving high performance in benchmark tasks.

How different AI models understand cells differently

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.

Unified multimodal learning enables generalized cellular response prediction to diverse perturbations

X-Pert is a unified framework for predicting cellular responses to diverse perturbations, outperforming existing methods across genetic and chemical perturbations and enabling generalization across cell types.

Benchmarking AI Models for In Silico Gene Perturbation of Cells

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

scDirect: key transcription factor identification for directing cell state transitions based on single-cell multi-omics data

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

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data

simCAS generates high-fidelity scCAS data using embeddings, enabling benchmarking of computational methods for cell clustering, trajectory inference, and cis-regulatory interaction analysis.

A generic reference defined by consensus peaks for scATAC-seq data analysis

The paper constructs a generic chromatin accessibility reference (cPeaks) from bulk ATAC-seq data to improve scATAC-seq analysis and cell type identification.

STEM: A Method for Mapping Single-cell and Spatial Transcriptomics Data with Transfer Learning

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