Zhi Huang

This study decomposes scanner batch effects in pathology foundation models and shows that image corrections based on measured properties improve alignment but do not fully match model representations.

This paper introduces Spatial Hi-C-RNA, a spatial multi-omics technology that co-maps chromatin conformation and transcriptome in tissue, revealing spatially coherent domains and new insights into gene regulation.

This study shows that MCPyV-driven tumors evolve immune-evasive traits under immune pressure, favoring sT over LTT expression, using a mouse model of Merkel cell carcinoma.

SpatialFinder uses a vision-language model with human feedback to prioritize high-value regions in H&E slides for spatial transcriptomics, improving ROI selection accuracy.

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

The paper identifies proteomic features of resilience to Alzheimer's disease, finding lower soluble Aβ levels and specific protein interactions in brain regions associated with resistance to dementia.

DEGAS transfers disease information from patients to cells using deep transfer learning, identifying PHF19high myeloma cells linked to progression.

This paper introduces scFEA, a graph neural network model to estimate cell-wise metabolic flux from scRNA-seq data, validated with matched metabolomics data and applied to multiple datasets.

MORONET is a multi-omics integration method using graph convolutional networks for biomedical classification, outperforming existing approaches and identifying disease-related biomarkers.

TSUNAMI is a tool for mining gene co-expression networks and performing downstream analysis, offering features like web interface, database access, and visualization.

LAmbDA reduces batch effects and improves subtype detection by integrating datasets with ambiguous labels across species and studies.