Shou‐Jiang Gao

This study uses spatial transcriptomics to compare how Asian and African Zika strains affect microglial states and brain tissue organization in mice, revealing strain-specific mechanisms of neuropathogenesis.

spatiAlytica enables non-programmer biologists to perform interactive spatial omics analysis using natural language and a viewer-centric agentic system.

The paper identifies tissue-conserved m6A sites in human cells and shows they regulate essential genes involved in core cellular processes and are disrupted in cancer.

STiLE automates TMA dearraying using cell centroid coordinates, achieving high accuracy and robustness across platforms and artifacts.

This paper presents a spatial single-cell atlas of Kaposi's sarcoma, identifying CD34+ progenitor lymphatic endothelial cells as primary targets of KSHV and revealing mechanisms of tumor progression and immune modulation.

This study compares two RNA modification detection models, Dorado and m6Anet, using RNA004 sequencing data, finding Dorado has higher recall but both have high false discovery rates for m6A and pseudouridine.

This study identifies FAM50A-mediated splicing reprogramming by KSHV as a mechanism to activate STAT3 and drive oncogenic transformation in mesenchymal stem cells.

This study identifies CASTOR1 as a tumor suppressor in NSCLC that modulates mTORC1 and KRAS signaling, and shows that its loss promotes tumorigenesis and resistance to KRAS inhibitors.

This study introduces GS-NN, a gene-set-based deep learning method to augment surface protein levels from scRNA-seq data, showing improved cross-subject performance and interpretability.

reguloGPT constructs a knowledge graph of molecular regulatory pathways using GPT-4 for entity recognition and relationship extraction, improving over existing methods and demonstrating utility in cancer research.

The paper develops a database linking cell surface proteins and transcription factors in immune cells using CITE-seq data from COVID-19 patients and healthy controls.

This paper introduces Deep-m6A, a deep learning model for predicting m6A sites and identifies 709 functionally significant m6A-regulated genes and nine subnetworks associated with cancer and other diseases.