Jiliang Tang

The paper presents a longitudinal multimodal benchmarking dataset and competition to evaluate modality prediction methods for gene regulatory inference, showing top models outperform existing approaches.

scLinguist translates between single-cell multi-omics modalities by pretraining on unimodal data and fine-tuning on paired data, outperforming existing methods in predicting protein abundance from RNA.

DANCE 2.0 automates and interprets single-cell preprocessing by recommending optimal pipelines for various tasks, improving performance across six major analyses.

This paper introduces Tabula, a privacy-preserving foundation model for single-cell transcriptomics using federated learning and tabular modeling, achieving better performance with less data and revealing regulatory logic across biological systems.

Focus improves cell type annotation by modeling subcellular RNA distribution in spatial transcriptomics data and identifies cell type-specific molecular pathways.

DANCE provides a benchmark platform for single-cell analysis with 32 methods across 21 datasets, enabling easy reproduction and development of deep learning models.

The paper introduces a GAN-based method to generate high-quality membranes for cells with poor staining in fluorescent microscopy images, improving segmentation accuracy.

This paper introduces a general single-cell analysis framework using conditional diffusion models, showing competitive performance across tasks with added flexibility via prior information.

Treating cells as tokens and tissues as sentences, with spatial transcriptomics added during pre-training, gives a single-cell foundation model the authors report outperforms existing models and runs far faster.

The paper introduces a large-scale spatial transcriptomic dataset and a Graph Neural Network-based method for cell type deconvolution in human immuno-oncology.