Our major research interest is to better understand the biology underlying cancer using quantitative approaches. We integrate multi-level (epi)genomic data with clinical outcomes to dissect cancer heterogeneity, identify biomarkers, and develop computational tools for precision oncology, bridging basic biological studies and clinical research.
Dissecting the molecular landscape of cancer through integrative analysis of multi-dimensional data to identify clinically actionable subtypes and regulatory mechanisms.
Multi-Omics Integration
Integrating genomic, epigenomic, transcriptomic, and proteomic data to comprehensively characterize tumor biology across colorectal, gastric, pancreatic, and esophageal cancers.
Network Biology
Applying probabilistic graphical models and network-based approaches to infer gene regulatory networks, identify master regulators, and prioritize therapeutic targets from multi-omics data.
Cancer Subtyping
Developing and benchmarking computational methods for identifying molecularly distinct cancer subtypes that relate to clinical responses and patient outcomes.
Developing non-invasive, blood-based diagnostic and prognostic signatures for early cancer detection and precision treatment stratification.
Circulating miRNA Signatures
Genome-wide discovery and multicenter validation of circulating microRNA biomarkers for early detection of colorectal, gastric, esophageal, and pancreatic cancers.
Exosome-Based Diagnostics
Leveraging extracellular vesicle transcriptomic profiling to develop exosome-based signatures for noninvasive cancer detection and treatment monitoring.
Treatment Response Prediction
Predicting therapeutic response and resistance using molecular signatures to guide precision oncology decisions for gastrointestinal cancers.
Developing and applying artificial intelligence methods to extract clinically relevant patterns from complex biomedical data, including histopathology images and multi-omics profiles.
Molecular Classification
Designing deep learning frameworks for cancer molecular subtyping that integrate multi-omics data for improved accuracy and clinical translation.
Digital Pathology & Imaging
Applying deep learning to histopathology images for spatial organization analysis, prognosis prediction, and biomarker discovery.
Neoantigen & Immunotherapy
Computational prediction of cancer neoantigens from genomic data and modeling of tumor-immune interactions to enable personalized immunotherapy.
Leveraging single-cell and spatial transcriptomics to resolve tumor heterogeneity at unprecedented resolution and uncover cell-type-specific regulatory programs.
Tumor Microenvironment
Dissecting cellular heterogeneity and functional states within the tumor microenvironment using single-cell RNA-seq to understand immune and stromal dynamics.
Spatial Transcriptomics
Analyzing spatially resolved gene expression to map tumor architecture and identify spatially regulated programs at the tissue level.
Host-Microbe Systems Biology
Investigating host-microbe interactions through multi-omics, including bacterial regulatory networks, virulence mechanisms, and infection-related host responses.
Designing targeted protein/RNA degradation therapeutics (PROTACs/RIBOTACs) and nanocarrier delivery systems integrated with AI and bioinformatics for precision cancer therapy.
RIBOTACs & PROTACs
Cell-selective delivery of targeted RNA and protein degraders via engineered nanobodies for pancreatic and other cancer treatments.
Nanocarrier Delivery
Developing LNP and nanobody-based delivery platforms for precision therapeutic targeting, integrating AI/machine learning for design optimization.
Ferroptosis & Cancer
Inducing ferroptosis in cancer cells through targeted degradation of glutathione peroxidase and other key regulators.
Computational Tools
DeepCC
Deep learning cancer subtyping · Oncogenesis 2019
HTSanalyzeR
Network analysis of HTS · Bioinformatics 2011
GCclassifier
Gastric cancer subtyping · CSBJ 2024
RegNetwork 2025
Gene regulatory network database · NAR 2026
PAGnet
Pathway & gene network analysis
PSRnet
Post-transcriptional regulation resource
CRC-SPA
Spatial profiling for CRC microenvironment
SV4GD
Structural variation database · NAR 2025