Feng GAO

Biography

Dr. Feng Gao is a Professor of Medical AI at the Sixth Affiliated Hospital of Sun Yat-sen University and the data analysis lead for the ICGC-ARGO Colorectal Cancer project. Leveraging his interdisciplinary expertise in computer science and oncology, Dr. Gao’s lab is dedicated to building the critical technical infrastructure for the “AI-Native Future Hospital.” By pioneering “AI Doctor 3.0” through multi-agent systems, multimodal foundation models, and intelligent surgical computing, his team is driving the paradigm shift from passive medical tools to autonomous, collaborative clinical intelligence. Using colorectal cancer as a flagship testing ground, Dr. Gao focuses on architecture-driven AI paradigms—rather than mere parameter scaling—to create full-stack, scalable solutions that fundamentally restructure healthcare supply and delivery.

Biography

Research Interests

Education

Highlighted publications

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

PCsRNAdb: a comprehensive resource of small noncoding RNAs across cancers

All related publications

Senescence-Based Colorectal Cancer Subtyping Reveals Distinct Molecular Characteristics and Therapeutic Strategies

MiR-423-5p is a novel endogenous control for the quantification of circulating miRNAs in human esophageal squamous cell carcinoma

An immune, stroma, and epithelial--mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II--III colorectal cancer

Editorial: Medical knowledge-assisted machine learning technologies in individualized medicine

Medical knowledge-assisted machine learning technologies in individualized medicine

Exploring multi-omics latent embedding spaces for characterizing tumor heterogeneity and tumoral fitness effects

CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study

Predicting prognosis and immunotherapy response among colorectal cancer patients based on a tumor immune microenvironment-related lncRNA signature

Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images

A transcription factor signature can identify the CMS4 subtype and stratify the prognostic risk of colorectal cancer

A tumor immune microenvironment-related lncRNA signature for the prognosis and immunotherapeutic sensitivity prediction in colorectal cancer.

Multi-size deep learning based preoperative computed tomography signature for prognosis prediction of colorectal cancer

PIANOS: A platform independent and normalization free single-sample classifier for colorectal cancer

The growth pattern of liver metastases on MRI predicts early recurrence in patients with colorectal cancer: a multicenter study

Cross-level contrastive learning and consistency constraint for semi-supervised medical image segmentation

DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients

A microRNA-based liquid biopsy signature for the early detection of esophageal squamous cell carcinoma: a retrospective, prospective and multicenter study

Single-cell RNA-seq recognized the initiator of epithelial ovarian cancer recurrence

Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion

Lesion-aware dynamic kernel for polyp segmentation