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Ying Xiong on Muck Rack

Ying Xiong

As seen in: Arthritis & Rheumatology, British Journal of Cancer, Color Research & Application, Nature, Wiley, Wiley Online Library, PLOS, PLOS ONE, ScienceDirect, Frontiers and
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Articles

A disease-centric vision-language foundation model for precision oncology in kidney cancer

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By Zhongwei Zhao, Zilong Wang, Xufang Luo, Feng Chen, Kang Wang, Chuanfu Wu, Xue Zhang, Shaoting Zhang, Jiaxi Yao, Xingwei Jin, Xinyang Jiang, Yifan Yang, Dongsheng Li, Lili Qiu, Zhiqiang Shao, Jianming Guo, Nengwang Yu, Ying Xiong, Shuo Wang, Yuhui Tao
| Nature Verified
Abstract The non-invasive assessment of renal masses remains a critical challenge in urologic oncology, where diagnostic uncertainty frequently causes overtreatment. Here, we develop RenalCLIP, a vision-language foundation model for precision oncology in kidney cancer. Utilizing 27,866 computed tomography scans from 8809 patients across diverse multi-center cohorts, we employ a two-stage pre-training strategy to align domain-specific visual and textual representations.
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AAV gene therapy for homozygous familial hypercholesterolemia: a phase 1 trial

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By Tao Zheng, Ge Gao, Chenbo Xu, Ruifeng Li, Jinrui Shao, Xiaoke Wang, Xiu Han, Juanli Li, Peining Liu, Ying Xiong, Lailing Song, Jiaqi Wang, Shengmei Wen, Xiaozhen Zhuo, Gang Wang, Lixin Jiang, Guang Qu, Tao Chen, Yongbai Luo, Zuyi Yuan, Yue Wu
| Nature Verified
Abstract Homozygous familial hypercholesterolemia (HoFH) is a rare autosomal disease characterized by severely elevated low-density lipoprotein cholesterol (LDL-C) levels and accelerated atherosclerotic cardiovascular disease. More than 80% of patients with HoFH carry low-density lipoprotein receptor (LDLR) mutations.
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ICIsAtlas reveals a suppressive NK cell niche in pan-cancer immunotherapy profiles

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By Yumo Xie, Jinxin Lin, Haotian Liu, Ying Xiong, Junyi Han, Ziying Huang, Jingrong Weng, Zixiao Wan, Puning Wang, Xiaoxia Liu, Linping Wu, Qian Cai, Yanxin Luo, Xiaolin Wang, Huichuan Yu, Peisi Li, Meijin Huang
| Nature Verified
Abstract Immune checkpoint inhibitors (ICIs) have reshaped the treatment in multiple tumors. However, a substantial proportion of patients exhibit limited responses. The lack of harmonized, large-scale pan-cancer ICI datasets coupled with accessible analysis tools hinders the systematic discovery of response biomarkers and resistance mechanisms.
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