1.Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
2.State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
3.National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Author bio:
* Fei Ma drmafei@126.com
Bing-He Xu xubinghebm@163.com
Hai-Li Qian qianhaili001@163.com
Funds:
the National Key Research and Development Program of China(2022YFE0103600;2024YFA1107400);the National Natural Science Foundation of China(82472633;92459304);the CAMS Innovation Fund for Medical Sciences(2022-I2M-2-001;2023-I2M-2-004)
Tao Yang, Yuan-Yi Wang, Fei Ma, 等. Build the virtual cell with artificial intelligence: a perspective for cancer research[J]. Military Medical Research, 2025,12(9):1492-1495.
Tao Yang, Yuan-Yi Wang, Fei Ma, et al. Build the virtual cell with artificial intelligence: a perspective for cancer research[J]. Military Medical Research, 2025, 12(9): 1492-1495.
Tao Yang, Yuan-Yi Wang, Fei Ma, 等. Build the virtual cell with artificial intelligence: a perspective for cancer research[J]. Military Medical Research, 2025,12(9):1492-1495. DOI: 10.1186/s40779-025-00591-6.
Tao Yang, Yuan-Yi Wang, Fei Ma, et al. Build the virtual cell with artificial intelligence: a perspective for cancer research[J]. Military Medical Research, 2025, 12(9): 1492-1495. DOI: 10.1186/s40779-025-00591-6.
Build the virtual cell with artificial intelligence: a perspective for cancer research
Digital in-line holographic microscopy for label-free identification and tracking of biological cells
PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer
Sepsis in burns: lessons learned, challenges remain
Enhancing the clinical relevance of haemorrhage prediction models in trauma
既有线提速路基动应力分析
相关作者
Bing-He Xu
Hai-Li Qian
Fei Ma
Sang Joon Lee
Jihwan Kim
Yong-Bo Xiang
Yu Wang
Xiao-Wei Chen
相关机构
Department of Mechanical Engineering, Pohang University of Science and Technology
Key Laboratory of Minimally Invasive Therapy Research for Lung Cancer, Chinese Academy of Medical Sciences
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College