Cheng-Hang LI

Cheng-Hang LI

Research Assistant

Cancer Institution PKU & HKUST Medical Center



Chenghang LI is a master of Computer Science and Technology co-supervised by the Sixth Affiliated Hospital and School Computer Science and Engineering at Sun Yat-sen University. He is interested in applying advanced machine learning technologies to medical research. His current research topics include supervised learning of cancer molecular subtypes with multi-omics data, survival analysis with genomic data and computational pathology images based on deep learning. He completed an MPhil in Artificial Intelligence at the Hong Kong University of Science and Technology (Guangzhou), where he deepened his expertise in deep learning and its applications in pathology and radiology. Now, he works at the Cancer Institute of Peking University & HKUST Medical Center. In his current role, he continues to focus on integrating artificial intelligence and machine learning techniques into oncology research.

Interests

  • Deep Learning
  • Feature Fusion

Education

  • Mphil in Artificial Intelligence, 2022 - 2024

    The Hong Kong University of Science and Technology Guangzhou

  • MEng in Computer Science and Technology, 2019 - 2022

    Sun Yat-sen University

  • BEng in Polymer Materials and Science, 2015 - 2019

    Sichuan University

Publications

Deciphering Tertiary Lymphoid Structure Heterogeneity Reveals Prognostic Signature and Therapeutic Potentials for Colorectal Cancer: A Multicenter Retrospective Cohort Study
Structure Embedded Nucleus Classification for Histopathology Images
Senescence-Based Colorectal Cancer Subtyping Reveals Distinct Molecular Characteristics and Therapeutic Strategies
An immune, stroma, and epithelial--mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II--III colorectal cancer
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
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
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
Deep learning to identify a gene signature associated with molecular subtypes that predicts prognosis in colorectal cancer.
Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.
A metabolism-related radiomics signature for predicting the prognosis of colorectal cancer
Integrating diffusion components of multi-omics datasets with application to cancer molecular subtyping
A signature of hypoxia-related factors reveals functional dysregulation and robustly predicts clinical outcomes in stage I/II colorectal cancer patients