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Yingjun Yang on Muck Rack

Yingjun Yang

As seen in: Nature, Wiley Online Library, MDPI, ACS Publications, Science Magazine, Royal Society of Chemistry, Chemistry World
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Articles

Genetic Analysis in Monochorionic Diamniotic Twins Discordant for One Fetus With Structural Anomalies

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By Luyao Cai, Yao Zhang, Yingjun Yang
| Wiley Online Library Verified
The first two authors contributed equally to this article. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available upon request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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A Multimodal large language model-based triage tool for osteoporotic vertebral compression fractures using posture and movement videos

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By Meiwei Zhang, Xiaoqing Jin, Shicai Xu, Xinlin Huang, Qiang Tang, Yu Zou, Lilan Liu, Yingjun Yang, Dejun Zhong, Chao Tang, Zhengli Zhong, Yehui Liao, Kaijia Huang
| Nature Verified
Abstract Osteoporotic vertebral compression fracture (OVCF) is common yet frequently underdiagnosed due to nonspecific symptoms and limited access to definitive imaging. We developed a two-stage screening framework that integrates prompt-guided multimodal large language model (LLM) feature extraction with machine-learning classification. In a multicenter cohort of 204 participants (102 OVCF, 102 controls), standardized posture images and functional videos were processed using pose estimation.
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Knowledge distillation for named entity recognition in traditional chinese medicine

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By Wangping Xiong, Hongda Huang, Yingjun Yang, Ming Yang Verified, Xiang Zhou, Shaobo Chen, Xin Cheng, Xian Zhou, Zhaoxing Xu
| Nature Verified
Abstract Named entity recognition (NER) in traditional Chinese medicine (TCM) text is central to the structuring and intelligent application of TCM knowledge. TCM case texts are characterized by sparse entity distributions and limited domain-specific data. Existing methods often struggle to recognize low-frequency entities and show limited capacity for effective knowledge transfer. This paper proposes a TCM NER framework incorporating structured knowledge distillation.
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