Yicheng Gao

CellHermes integrates multimodal omics data using a biological language model, achieving comparable or better performance than single-cell models across various tasks.

CausCell improves explainability and controllability of single-cell data through causal disentanglement learning and counterfactual generation.

This study introduces STAMP, a novel method for genetic perturbation outcome prediction that improves accuracy across three subtasks and provides a benchmarking framework for evaluating models.

PerturBase is a database that compiles and visualizes single-cell perturbation data from multiple studies, enabling analysis of genetic and chemical perturbations across millions of cells.

The paper clarifies that improper negative data sampling strategies can bias TCR-antigen prediction models like PanPep and advocates for consistent comparison methods.

UniTCR integrates T-cell receptor and transcriptome data using contrastive learning for diverse immunology tasks, showing superior performance on paired datasets.

This study introduces TCRBagger, a weakly-supervised framework for peptide-TCR binding prediction to improve neo-epitope identification in tumor immunotherapy.

scPrivacy enables privacy-preserving integration of single-cell RNA-seq datasets from multiple institutions for cell type identification.