Feng GAO

Feng GAO

Associate Professor

Sun Yat-sen University



Dr. Feng GAO is currently an Associate Professor in the Department of Colorectal Surgery at the Sixth Affiliated Hospital, awarded by 100 Top Talents Program of Sun Yat-sen University. He was trained as a computer engineer initially and had his earliest career in finance. Thereafter, he pursed his PhD in the filed of cancer research by combining advanced artificial intelligence and big data technologies. Dr. GAO is a core member of the ICGC-ARGO CRC project. His major research interest is to develop artificial intelligence for diagnosis and prognosis in colorectal cancer.

Interests

  • Artificial Intelligence
  • Colorectal Cancer

Education

  • Joint PhD in Cancer Biology and Bioinformatics, 2015 - 2018

  • Visiting Student in Computer Science and Electronic Engineering, 2007 - 2008

  • BEng in Computer Science and Technology, 2005 - 2009

Publications

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
Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer
The growth pattern of liver metastases on MRI predicts early recurrence in patients with colorectal cancer: a multicenter study
DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion
The enhanced cell cycle related to the response to adjuvant therapy in pancreatic ductal adenocarcinoma
Identification of an Autophagy-Related Gene Signature for the Prediction of Prognosis in Early-Stage Colorectal Cancer
Cancer-associated fibroblasts impact the clinical outcome and treatment response in colorectal cancer via immune system modulation: a comprehensive genome-wide analysis
Genome-Wide Analysis Reveals Hypoxic Microenvironment Is Associated With Immunosuppression in Poor Survival of Stage II/III Colorectal Cancer Patients
Predicting treatment response from longitudinal images using multi-task deep learning
A novel cell-free DNA methylation-based model improves the early detection of colorectal cancer
Identification of prognostic spatial organization features in colorectal cancer microenvironment using deep learning on histopathology images
Protein-protein interaction analysis reveals a novel cancer stem cell related target TMEM17 in colorectal cancer
A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer
Immune-related gene signature in predicting prognosis of early-stage colorectal cancer patients
Hippo-YAP signaling controls lineage differentiation of mouse embryonic stem cells through modulating the formation of super-enhancers
Integrating diffusion components of multi-omics datasets with application to cancer molecular subtyping
Development of a novel liquid biopsy test to diagnose and locate gastrointestinal cancers
Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs
Development and validation of an individualized gene expression-based signature to predict overall survival in metastatic colorectal cancer
Immune-related gene signature for predicting the prognosis of head and neck squamous cell carcinoma
Multiomics-Based Colorectal Cancer Molecular Subtyping Using Local Scaling Network Fusion
A signature of hypoxia-related factors reveals functional dysregulation and robustly predicts clinical outcomes in stage I/II colorectal cancer patients
Long-Read RNA Sequencing Identifies Alternative Splice Variants in Hepatocellular Carcinoma and Tumor-Specific Isoforms
An immune-related gene pairs signature predicts overall survival in serous ovarian carcinoma
DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
An immune-related gene signature predicts prognosis of gastric cancer
Gene Expression Signature in Surgical Tissues and Endoscopic Biopsies Identifies High-Risk T1 Colorectal Cancers
Pathway analysis of hypoxia-related factors in early colorectal cancer patients with poor prognosis
Immune-related gene signature in predicting prognosis of early-stage colorectal cancer patients
RNAMethyPro: a biologically conserved signature of N6-methyladenosine regulators for predicting survival at pan-cancer level
Genome-wide Discovery of a Novel Gene-expression Signature for the Identification of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma
A genomewide transcriptomic approach identifies a novel gene expression signature for the detection of lymph node metastasis in patients with early stage gastric cancer
Retinoic acid and 6-formylindolo(3,2-b)carbazole (FICZ) combination therapy reveals putative targets for enhancing response in non-APL AML
High-throughput three-dimensional chemotactic assays reveal steepness-dependent complexity in neuronal sensation to molecular gradients
Dissecting cancer heterogeneity based on dimension reduction of transcriptomic profiles using extreme learning machines
Genome-wide Discovery and Identification of a Novel miRNA Signature for Recurrence Prediction in Stage II and III Colorectal Cancer
A Novel Non-Invasive Circulating Mirna Signature for Detection of Esophageal Adenocarcinoma
A MicroRNA Signature Associated With Metastasis of T1 Colorectal Cancers to Lymph Nodes
Poor-prognosis nasopharyngeal carcinoma as defined by a molecularly distinct subgroup and prediction by a miRNA expression signature
Identification, Development and Validation of a Circulating Mirna-Based Diagnostic Signature for Early Detection of Gastric Cancer
A Novel Mirna Signature for the Detection of Lymph Node Metastasis in Submucosal Colorectal Cancer Patients
A Novel Mirna-Based, Non-Invasive, Diagnostic Panel for Detection of Esophageal Squamous Cell Carcinoma
Genome-Wide Analysis Revealed a Robust Gene Expression Signature to Identify Lymph Node Metastasis in Submucosal Colorectal Cancer
Genome-wide discovery and identification of a novel microrna signature for recurrence prediction in colorectal cancer