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, supported by Sun Yat-sen University’s prestigious 100 Top Talents Program. Initially trained as a computer engineer, Dr. Gao briefly worked in engineering before transitioning to a role in finance. He later pursued a Ph.D. in cancer research, focusing on the integration of advanced artificial intelligence and big data technologies. As a core member of the ICGC-ARGO CRC project, his primary research interest lies in developing AI-driven solutions for the diagnosis and prognosis of colorectal cancer. He is also keenly interested in applying cutting-edge technologies to other medical fields, including diabetes, reproductive health, pediatrics, and dentistry.

Interests

  • Artificial Intelligence
  • Colorectal Cancer

Education

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

    City University of Hong Kong & Cornell University

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

    Kumamoto University

  • BEng in Computer Science and Technology, 2005 - 2009

    Shandong University

Publications

Deciphering Tertiary Lymphoid Structure Heterogeneity Reveals Prognostic Signature and Therapeutic Potentials for Colorectal Cancer: A Multicenter Retrospective Cohort Study
TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Structure Embedded Nucleus Classification for Histopathology Images
Cross-Level Contrastive Learning and Consistency Constraint for Semi-Supervised Medical Image Segmentation
Genome-wide study reveals novel roles for formin-2 in axon regeneration as a microtubule dynamics regulator and therapeutic target for nerve repair
A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles
Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy
The landscape of objective response rate of anti-PD-1/L1 monotherapy across 31 types of cancer: a system review and novel biomarker investigating
Senescence-based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies
Deep learning-derived spatial organization features on histopathology images to predict prognosis in patients with colorectal liver metastasis, after hepatectomy.
Immunological profiling of human colorectal cancer defines four distinct molecular subtypes
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
Medical knowledge-assisted machine learning technologies in individualized medicine
Multi-scope Analysis Driven Hierarchical Graph Transformer for Whole Slide Image Based Cancer Survival Prediction
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
Cancer-associated fibroblasts impact the clinical outcome and treatment response in colorectal cancer via immune system modulation: a comprehensive genome-wide analysis
OCaMIR—a noninvasive, diagnostic signature for early-stage ovarian cancer: a multi-cohort retrospective and prospective study
An interpretable computer-aided diagnosis method for periodontitis from panoramic radiographs
Integrated immune-related gene signature to predict clinical outcome for patients with luminal B breast cancer.
A model combing an immune-related genes signature and an extracellular matrix-related genes signature in predicting prognosis of left-and right-sided colon cancer
Su029 INTEGRATING PROGNOSIS-RELATED GENES WITH IMMUNE-RELATED GENE SIGNATURE FOR SURVIVAL STRATIFICATION OF EARLY-STAGE COLORECTAL CANCER
Deep transformers for fast small intestine grounding in capsule endoscope video
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
Genome-wide Discovery of MicroRNA Biomarkers for Cancer Precision Medicine
Immune-related gene signature in predicting prognosis of early-stage colorectal cancer patients
Multiomics-based colorectal cancer molecular subtyping using local scaling network fusion
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
Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs
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
Genome-wide discovery of a novel gene-expression signature for the identification of lymph node metastasis in esophageal squamous cell carcinoma
RNAMethyPro: a biologically conserved signature of N6-methyladenosine regulators for predicting survival at pan-cancer level
Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities
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
Potential for subsets of wt-NPM1 primary AML blasts to respond to retinoic acid treatment
HTSanalyzeR2: An Ultra-Fast R/Bioconductor Package for High-Throughput Screens with Interactive Report
Poor-prognosis nasopharyngeal carcinoma as defined by a molecularly distinct subgroup and prediction by a miRNA expression signature
A Novel Mirna Signature for the Detection of Lymph Node Metastasis in Submucosal Colorectal Cancer Patients
Genome-Wide Analysis Revealed a Robust Gene Expression Signature to Identify Lymph Node Metastasis in Submucosal Colorectal Cancer
Identification, Development and Validation of a Circulating Mirna-Based Diagnostic Signature for Early Detection of Gastric Cancer
A Novel Mirna-Based, Non-Invasive, Diagnostic Panel for Detection of Esophageal Squamous Cell Carcinoma
Genome-wide discovery and identification of a novel microrna signature for recurrence prediction in colorectal cancer
Part 1 Biomarkers for Precision Medicine