Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses
Shuyu Li(Xuzhou Medical College), Zhifang Yang(China Coal Research Institute (China)), Wantao Wu(Central South University), Xisong Liang(Central South University), Peng Luo(Zhujiang Hospital), Hanning Li(Tongji Hospital), Nan Zhang(Huazhong University of Science and Technology), Bo Zhang(Hebei Medical University), Zeyu Wang(Anhui University), Xun Zhang(Zaozhuang University), Hao Zhang(Northwestern Polytechnical University), Ran Zhou(Central South University), Xue Yang(Tongji Hospital), Zirui Li(Hebei University), Jie Wen(Central South University), Quan Cheng(Central South University), Qi Zhang(Jiangnan University), Ziyu Dai(Central South University)
Cited by 17
Related Papers
Isolation and retrieval of circulating tumor cells using centrifugal forces
|Scientific Reports|2013|732
Glioma targeted therapy: insight into future of molecular approaches
|Molecular Cancer|2022|727
PLGA-based biodegradable microspheres in drug delivery: recent advances in research and application
|Drug Delivery|2021|578
PARP inhibitor resistance: the underlying mechanisms and clinical implications
|Molecular Cancer|2020|548
Regulatory mechanisms of immune checkpoints PD-L1 and CTLA-4 in cancer
|Journal of Experimental & Clinical Cancer Research|2021|535