Ongoing research
01
Spatial multi-omics
Spatial multi-omics is a cutting-edge technology that measures the molecular profiles of tissues while providing information of the cell spatial locations. This powerful approach is transforming how we see and understand disease tissues. However, the complexity and scale of spatial muli-omics data present significant analytical challenges that limit its full potential. Our lab develops computational tools to solve the challenges and extract meaningful patterns from these rich datasets. Through these efforts, we aim to unlock new insights into cancer biology and advance drug discovery.
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CELESTA: https://www.nature.com/articles/s41592-022-01498-z
MONTAGE: https://www.biorxiv.org/content/10.1101/2025.04.30.651486v1
02
Multi-modality learning
We develop multi-modality learning approaches that integrate spatial multi-omics, radiology, pathology, and patient clinical data to create predictive models. We aim at uncovering spatially resolved molecular information predictive for imaging features, treatment responses, and clinical outcomes. This work bridges the gap between disease biology and clinical assessment. Through collaborations with interdisciplinary experts, our goal is to turn this integrated view into powerful tools for discovering new biomarkers and advancing personalized care.
03
Omics information cascade in cancer
Cancer is shaped by changes that flow from our genes to the proteins and to the metabolites that power and build our cells. Each layer in this information cascade holds unique clues about tumor initiation and progression, such as from genetic mutations to altered protein-protein interactions and metabolic shifts visible in PET radiology scans. Traditionally studied in isolation, these layers reveal far more when integrated. Our project connects these data to build a more complete view of cancer biology, and works with experimental collaborators to uncover new mechanisms that drive tumor initiation, growth and metastasis.
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"GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma", Zhang et al., (2018)