Guoxin Cai

ORIGAMI reconstructs cis-regulatory networks using multi-omics data and deep learning, revealing synergistic transcription control and disease-associated rewiring.

CHORD integrates cross-species single-cell transcriptomic data to learn gene, cell, and cell-type representations, enabling cell-type annotation and cross-species comparison of developmental and phenotypic patterns.

TopicVI identifies context-specific gene programs in single-cell and spatial transcriptomics data by integrating biological knowledge with data-driven refinement.

This paper shows that IL-21-Fc reprograms NK cell metabolism to enhance their function against MHC-I deficient tumors, leading to tumor eradication in preclinical models.

Spanve is a nonparametric statistical method that identifies spatially variable genes in spatial transcriptomic data with fewer false positives, enabling better downstream analysis.

MATTE identifies differentially expressed genes and modules in noisy transcriptome data, improving analysis of phenotype-related biological insights.