
Biography
Dr. Feng Gao is a Professor of Medical AI at the Sixth Affiliated Hospital of Sun Yat-sen University and leads data analysis for the colorectal cancer program of the International Cancer Genome Consortium's Accelerating Research in Genomic Oncology initiative (ICGC-ARGO). Trained in computer science, cancer biology, and bioinformatics, he develops explainable and multimodal artificial intelligence for cancer research and clinical care. His lab uses colorectal cancer as a flagship setting, with work spanning disease mechanisms, medical imaging, clinical prediction, intelligent surgery, and AI systems that support complex medical and research tasks under expert supervision. The long-term goal is to improve healthcare quality and efficiency, expand access to expert care, and keep evidence, professional judgment, and human responsibility explicit. This work contributes to the technical foundations of the AI-Native Future Hospital.
Research Interests
- Artificial Intelligence
- Colorectal Cancer
Education
- Joint PhD in Cancer Biology and BioinformaticsCity University of Hong Kong & Cornell University2015 - 2018
- Visiting Student in Computer Science and Electronic EngineeringKumamoto University2007 - 2008
- BEng in Computer Science and TechnologyShandong University2005 - 2009
Lab highlights
Highlighted publications
All related publications
Cell-Selective Delivery of RIBOTACs via an Anti-EGFR Nanobody for Pancreatic Cancer Treatment
In Vivo Spatial Transcriptomics for Bleeding-free Profiling Human Internal Organs
UNRAVELING THE IMPACT OF INSOMNIA ON COLORECTAL CANCER: MICROBIOME DYSBIOSIS, IMMUNOSUPPRESSIVE MICROENVIRONMENT, AND THERAPEUTIC IMPLICATIONS
Real-world performance of open-source large language models in diabetes diagnosis
INTELCAPE: A Deep Learning-Powered System for Automated, High-Accuracy Crohn's Disease Diagnosis via Capsule Endoscopy
Multi-omics driven immune classification of colorectal cancer: Implications for immunotherapy efficacy prediction and enhancement with WNT signaling inhibition
CT4CMS: Preoperative Computed Tomography-Based Consensus Molecular Subtyping Prediction in Colorectal Cancer Using Interpretable Deep Learning
Lactylation of SLC26A3 in the acidic tumor microenvironment promotes malignant progression of colorectal carcinoma
Translating Molecular Subtypes into Cost-Effective Radiogenomic Biomarkers for Prognosis of Colorectal Cancer
Decoding Senescence-Driven Heterogeneity in Early-Onset Colorectal Cancer for Prognostic and Therapeutic Stratification
Predominant mutated non-canonical tumor-specific antigens identified by proteogenomics demonstrate immunogenicity and tumor suppression in CRC
Accurate Boundary Alignment and Realism Enhancement for Colonoscopic Polyp Image-Mask Pair Generation
FSA-Net: Fractal-driven Synergistic Anatomy-aware Network for Segmenting White Line of Toldt in Laparoscopic Images
Bridging Knowledge Discrepancy in Retinal Image Analysis through Federated Multi-Task Learning
A plasma metabolite-based test to detect minimal residual disease in post-surgery patients with colorectal cancer
PCsRNAdb: a comprehensive resource of small noncoding RNAs across cancers
Immunological Microenvironment Differences Between Left and Right Colon Cancer: Dynamic Interactions of CASC15+KLK6+ Epithelial Subpopulation with T Cells and Mast Cells
The role of tertiary lymphoid structures in renal cell carcinoma: From predictive biomarker to therapeutic target
Integrating prognosis-related genes with immune-related gene signature for development and validation of a survival stratification model for early-stage colorectal cancer
CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self-Supervised Learning
Showing the 20 most recent of 124 related publications.