Xin Wang

This study uses temporal multi-modal single-cell profiling to show CAR-T cells interact dynamically with glioblastoma cells, including antigen-negative ones, and reduce their proliferation through IFN-gamma.

NanoLAS 2.0 updates a nanobody database with improved search, 3D visualization, docking simulation, and epitope prediction tools.

The paper introduces an in silico screening strategy to optimize stable conformation of anti-SARS-CoV-2 nanobodies through iterative docking and analysis.

NanoLAS integrates and consolidates nanobody data to provide a comprehensive database with improved search and display features, enhancing research and application potential.

NanoBERTa-ASP predicts nanobody-antigen binding sites using a BERT-based model trained specifically on nanobody sequences, achieving high precision and AUC.

Nanotope predicts nanobody paratopes using graph neural networks and sequence/structural features, achieving higher accuracy than traditional methods without requiring antigen data.

The paper creates a high-resolution cellular atlas of healthy and injured kidney cell states and niches using single-cell and spatial technologies, identifying molecular signatures and pathways related to kidney injury.

The paper benchmarks five deep-learning-based protein folding algorithms on nanobody sequences to compare their accuracy against experimental structures.

This study uses spike-in normalization in single-cell RNA-seq to reveal dynamic transcriptional changes in cancer cells responding to anti-cancer drugs, identifying gene states and regulatory modules linked to drug efficacy.

PBrowse enables real-time collaborative exploration of genomic data through a web-based platform with integrated file sharing and discussion features.