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 StudyJia-Xin Lei#, Runxian Wang#, Chuling Hu, Xiaoying Lou, Min-Yi Lv, Chenghang Li, Baowen Gai, Xiao-Jian Wu*, Ruoxu Dou*, Du Cai*, Feng Gao*
PMID
DOI
International Journal of Surgery 2024
Senescence-Based Colorectal Cancer Subtyping Reveals Distinct Molecular Characteristics and Therapeutic StrategiesMin-Yi Lv,
Du Cai,
Cheng-Hang Li,
Junguo Chen,
Guanman Li,
Chuling Hu,
Baowen Gai,
Jiaxin Lei,
Ping Lan,
Feng Gao,
others DOI
Gastroenterology 2023
An immune, stroma, and epithelial--mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II--III colorectal cancerDu Cai,
Wei Wang,
Min-Er Zhong,
Dejun Fan,
Xuanhui Liu,
Cheng-Hang Li,
Ze-Ping Huang,
Qiqi Zhu,
Min-Yi Lv,
Chuling Hu,
others CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective studyMin-Er Zhong#, Xin Duan#, Ma-yi-di-li Ni-jia-ti, Haoning Qi, Dongwei Xu, Du Cai, Chenghang Li, Zeping Huang, Qiqi Zhu, Feng Gao*, Xiaojian Wu*
DOI
Journal of Translational Medicine 2022
A model combing an immune-related genes signature and an extracellular matrix-related genes signature in predicting prognosis of left-and right-sided colon cancerMin-Er Zhong, Du Cai, Dejun Fan, Wei Wang, Cheng-Hang Li, Ze-Ping Huang, Qiqi Zhu, Min-Yi Lv, Chuling Hu, Xiaojian Wu, Feng Gao
DOI
Journal of Clinical Oncology 2021
Deep learning to identify a gene signature associated with molecular subtypes that predicts prognosis in colorectal cancer.Du Cai,
Wei Wang,
Min-Er Zhong,
Dejun Fan,
Xuanhui Liu,
Cheng-Hang Li,
Ze-Ping Huang,
Qiqi Zhu,
Min-Yi Lv,
Chuling Hu,
others DOI
Journal of Clinical Oncology 2021
Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.Qiqi Zhu,
Du Cai,
Wei Wang,
Min-Er Zhong,
Dejun Fan,
Xuanhui Liu,
Cheng-Hang Li,
Ze-Ping Huang,
Min-Yi Lv,
Chuling Hu,
others DOI
Journal of Clinical Oncology 2021
A metabolism-related radiomics signature for predicting the prognosis of colorectal cancerDu Cai,
Xin Duan,
Wei Wang,
Ze-Ping Huang,
Qiqi Zhu,
Min-Er Zhong,
Min-Yi Lv,
Cheng-Hang Li,
Wei-Bin Kou,
Xiao-Jian Wu,
others DOI
Frontiers in molecular biosciences 2021
A signature of hypoxia-related factors reveals functional dysregulation and robustly predicts clinical outcomes in stage I/II colorectal cancer patientsYi-Feng Zou, Yu-Ming Rong, Ying-Xin Tan, Jian Xiao, Zhao-Liang Yu, Yu-Feng Chen, Jia Ke, Cheng-Hang Li, Xi Chen, Xiao-Jian Wu, Ping Lan, Xu-Tao Lin*, Feng Gao*
PMID
DOI
Cancer Cell International 2019