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Weiping Wang on Muck Rack

Weiping Wang

As seen in: AIChE Journal, Nature, Wiley Online Library, arXiv, Frontiers, Frontiers in Medicine, Frontiers in Immunology, MDPI, ACS Publications, Genome Biology and
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Articles

Pretreatment CT radiomics for prediction of pathological complete response after neoadjuvant chemoimmunotherapy in NSCLC: a multicenter study incorporating habitat analysis

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By Sunyi Zheng, Shuyuan Huang, Jiahui Zhang, Xiaomeng Yang, Pengchun Ye, Weiping Wang, Wenhua Li, Houpu Liu, Chunlin Zhang, Guoqing Liao, Haiyu Zhou, Jing Wang, Xiaonan Cui, Dongsheng Yue
| Frontiers in Immunology
Abstract Background: Pathological complete response (pCR) after neoadjuvant chemoimmunotherapy (NACI) varies among patients with non-small cell lung cancer (NSCLC). This study aimed to quantify intratumoral heterogeneity using habitat analysis and to develop a pretreatment model for predicting pCR. Methods: In this retrospective multicenter study, 287 patients with NSCLC who received NACI followed by surgery were included.
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Correction: Development of a multidisciplinary competency framework for specialist nurses in outpatient dental sedation and anesthesia: a mixed-methods Delphi and AHP study

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By Kai Li, Yuxue Wu, Shunyi Li, Weiping Wang, Ming Yi, Lin Fan
| Frontiers in Medicine
A Correction on Development of a multidisciplinary competency framework for specialist nurses in outpatient dental sedation and anesthesia: a mixed-methods Delphi and AHP study by Li, K., Wu, Y., Li, S., Wang, W., Fan, L., and Yi, M. (2026). Front. Med 13:1835046. doi: 10.3389/fmed.2026.1835046 Author Lin Fan was erroneously omitted as corresponding author. The correct corresponding authors are Ming Yi and Lin Fan.
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Computer Science > Computational Engineering, Finance, and Science

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By Kaiqi Zhao, Weiping Wang, Yunpeng Wang
| arXiv
Causal discovery at large spatiotemporal scale is difficult: variables are physically embedded, candidate interactions grow quadratically with system size, and causal structure changes with system state. We introduce GeoDCD, a geometry-aware neural framework that uses spatial coordinates to initialize a learnable hierarchy and converts a trained nonlinear predictor into time-varying directed graphs through input-output Jacobian sensitivity analysis.
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