News

10/17/2025: AI in Genomics Lab attended ASHG 2025

Oral talks presented by lab members: Chenyang Yuan: mcDETECT: Characterizing mRNA Localization in Polarized Neuronal Compartments with Spatial Transcriptomics. Shihan Liu: BRIDGE: Inferring 3D Molecular Tissue Structure from Sparse Spatial Omics and Histological Sections Posters presented by lab members: Jing Huang: HAT: Automated Pathologist-Guided Label Transfer for Multi-Study, Multi-Sample, and Multi-Status Spatial Omics (Board 1109W)

10/1/2025: Dr. Hu Receives 2025 NACC New Investigator Award

Dr. Hu, has been selected as a recipient of the 2025 National Alzheimer’s Coordinating Center (NACC) New Investigator Award. This highly competitive award provides funding to support innovative research in Alzheimer’s disease and related dementias, thanks to support from the Alzheimer’s Association® and NACC. This year’s competition drew 138 proposals from early-career investigators across ADRCs […]

7/1/2025: New Nature Communication Paper!

Excited to share our latest #genomics work published in Nature Communications! A fundamental question in spatial multi-omics analysis is: how exactly do molecular features shape tissue architecture? Quantitative measurement of the relationship is helpful, but not sufficient—we also need to visualize it. As humans are naturally visual thinkers, seeing how tissue structure shifts with molecular […]

1/15/2025: New Nature Methods Paper on multi-omics integration!

Excited to share our latest #genomics work published in Nature Methods. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology […]

08/25/2024: New Nature Communications Paper on Deep Profiling of Tumor Ecosystems

Excited to share our new article on building and evaluating METI, an end-to-end framework for deep profiling of tumor ecosystems using spatial transcriptomics. We’ve built on our previous success in creating super-resolution gene expression images, combined with advances in interpretable computational pathology, to enable METI to map cancer cells and TME components, stratify cell types […]