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Sujuan Tang

Showing results (1-10 of 7) with videos related to

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Scientific Reports|December 10, 2025
Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associationsQingwen Wu, Sujuan Tang
Scientific Reports|March 26, 2025
Research on recognition of diabetic retinopathy hemorrhage lesions based on fine tuning of segment anything modelSujuan Tang, Qingwen Wu
Scientific Reports|January 14, 2026
Self-supervised learning on graphs predicts non-coding RNA and disease associationsQingwen Wu, Sujuan Tang
Bioscience Reports|April 23, 2020
From sepsis to acute respiratory distress syndrome (ARDS): emerging preventive strategies based on molecular and genetic researchesQinghe Hu, Cuiping Hao, Sujuan Tang
Iscience|May 1, 2026
Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasetsQingwen Wu, Ziyou Qi, Qingwei Li, et al.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue|May 16, 2022
[Construction of a predictive model for early acute kidney injury risk in intensive care unit septic shock patients based on machine learning]Suzhen Zhang, Sujuan Tang, Shan Rong, et al.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue|August 7, 2023
[Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning]Manchen Zhu, Chunying Hu, Yinyan He, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Scientific Reports|December 10, 2025
Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associationsQingwen Wu, Sujuan Tang
Scientific Reports|March 26, 2025
Research on recognition of diabetic retinopathy hemorrhage lesions based on fine tuning of segment anything modelSujuan Tang, Qingwen Wu
Scientific Reports|January 14, 2026
Self-supervised learning on graphs predicts non-coding RNA and disease associationsQingwen Wu, Sujuan Tang
Bioscience Reports|April 23, 2020
From sepsis to acute respiratory distress syndrome (ARDS): emerging preventive strategies based on molecular and genetic researchesQinghe Hu, Cuiping Hao, Sujuan Tang
Iscience|May 1, 2026
Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasetsQingwen Wu, Ziyou Qi, Qingwei Li, et al.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue|May 16, 2022
[Construction of a predictive model for early acute kidney injury risk in intensive care unit septic shock patients based on machine learning]Suzhen Zhang, Sujuan Tang, Shan Rong, et al.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue|August 7, 2023
[Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning]Manchen Zhu, Chunying Hu, Yinyan He, et al.
Pageof 1