Ke Ni

The paper shows that EMT paths are influenced by cell cycle phase coupling and highlights the limitations of low-dimensional representations in capturing transition dynamics.

GraphVelo extends RNA velocity inference to multimodal data by using graph-based machine learning to preserve vector information across different data representations.

AtlasAgent uses a vision-language model to rapidly evaluate large-scale single-cell data integration, outperforming traditional methods in speed and efficiency.

LivecellX corrects segmentation and tracking errors in live-cell imaging using deep learning and provides tools for lineage reconstruction and dynamic analysis.

This paper introduces scRNAkinetics, a method to estimate RNA kinetics parameters from scRNA-seq data using biological priors, showing accurate inference on simulated datasets and validation with existing studies.

This paper introduces RADO, a doublet detection algorithm for single-cell sequencing data that maximizes the area under the precision-recall curve, outperforming existing methods on multiple datasets.

The paper analyzes single-cell RNA sequencing data using transition path theory to study gene regulation during cell phenotypic transitions, finding increased intercommunity regulation and GRN frustration.