Search Results - Qin, XiaoLin

Interpretable instance disease prediction based on causal feature selection and effect analysis by Chen, YuWen, Zhang, Ju, Qin, XiaoLin

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catena-Poly[[triaquacopper(II)]-μ(2)-furan-2,5-dicarboxyl­ato-κ(4) O (1),O (2):O (2),O (2′)] by Li, Ya-Feng, Gao, Yue, Xu, Yue, Qin, Xiao-lin, Gao, Wen-Yuan

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catena-Poly[(triaqua­zinc)-μ-furan-2,5-dicarboxyl­ato-κ(3) O (2):O (2),O (2′)] by Li, Ya-Feng, Gao, Yue, Xu, Yue, Qin, Xiao-Lin, Gao, Wen-Yuan

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catena-Poly[(aqua­dimethano­lzinc)-μ-furan-2,5-dicarboxyl­ato-κ(3) O (2):O (2),O (2′)] by Li, Ya-Feng, Gao, Yue, Xu, Yue, Qin, Xiao-Lin, Gao, Wen-Yuan

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catena-Poly[[[aqua­bis­(1H-imidazole-κN (3))copper(II)]-μ-furan-2,5-di­car­boxylato-κ(2) O (2):O (5)] trihydrate] by Li, Ya-Feng, Xu, Yue, Qin, Xiao-Lin, Gao, Wen-Yuan, Gao, Yue

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catena-Poly[[(1,10-phenanthroline-κ(2) N,N′)zinc]-μ-furan-2,5-dicarboxyl­ato-κ(4) O (2),O (2′):O (5),O (5′)] by Li, Ya-Feng, Xu, Yue, Qin, Xiao-Lin, Gao, Wen-Yuan, Gao, Yue

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catena-Poly[[(1,3-dimethyl­imidazolin-2-one-κN)(1,10-phenanthroline-κ(2) N,N′)copper(II)]-μ-furan-2,5-dicarboxyl­ato-κ(2) O (2):O (5)] by Li, Ya-Feng, Xu, Yue, Qin, Xiao-Lin, Yuan, Yong-Peng, Gao, Wen-Yuan

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catena-Poly[[[aqua­(2,2′-bipyridine-κ(2) N,N′)copper(II)]-μ-furan-2,5-di­carboxyl­ato-κ(2) O (2):O (5)] dihydrate] by Li, Ya-Feng, Xu, Yue, Qin, Xiao-Lin, Yuan, Yong-Peng, Gao, Wen-Yuan

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catena-Poly[[[aqua­(2,2′-bipyridine-κ(2) N,N′)zinc]-μ-furan-2,5-dicarboxyl­ato-κ(2) O (2):O (5)] dihydrate] by Li, Ya-Feng, Xu, Yue, Qin, Xiao-Lin, Yuan, Yong-Peng, Gao, Wen-Yuan

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Poly[[μ-aqua-diaqua­bis­(μ-furan-2,5-dicarboxyl­ato-κ(2) O (2):O (5))bis­(1,10-phenanthroline-κ(2) N,N′)dicopper(II)] N,N-dimethyl­formamide monosolvate] by Li, Ya-Feng, Qin, Xiao-Lin, Xu, Yue, Yuan, Yong-Peng, Gao, Wen-Yuan

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Clinical Performance of the Xpert(®) CT/NG Test for Detection of Chlamydia trachomatis and Neisseria gonorrhoeae: A Multicenter Evaluation in Chinese Urban Hospitals by Han, Yan, Shi, Mei-Qin, Jiang, Qing-Ping, Le, Wen-Jing, Qin, Xiao-Lin, Xiong, Han-Zhen, Zheng, He-Ping, Tenover, Fred C., Tang, Yi-Wei, Yin, Yue-Ping

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Comparison of Microdilution Method with Agar Dilution Method for Antibiotic Susceptibility Test of Neisseria gonorrhoeae by Chen, Shao-Chun, Liu, Jing-Wei, Wu, Xing-Zhong, Cao, Wen-Ling, Wang, Feng, Huang, Jin-Mei, Han, Yan, Zhu, Xiao-Yu, Zhu, Bang-Yong, Gan, Quan, Tang, Xiao-Zheng, Shen, Xing, Qin, Xiao-Lin, Yu, Yu-Qi, Zheng, He-Ping, Yin, Yue-Ping

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Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network by Chen, Yu-wen, Li, Yu-jie, Deng, Peng, Yang, Zhi-yong, Zhong, Kun-hua, Zhang, Li-ge, Chen, Yang, Zhi, Hong-yu, Hu, Xiao-yan, Gu, Jian-teng, Ning, Jiao-lin, Lu, Kai-zhi, Zhang, Ju, Xia, Zheng-yuan, Qin, Xiao-lin, Yi, Bin

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A Simple and Quick Screening Method for Intrapulmonary Vascular Dilation in Cirrhotic Patients Based on Machine Learning by Li, Yu-Jie, Zhong, Kun-Hua, Bai, Xue-Hong, Tang, Xi, Li, Peng, Yang, Zhi-Yong, Zhi, Hong-Yu, Li, Xiao-Jun, Chen, Yang, Deng, Peng, Qin, Xiao-Lin, Gu, Jian-Teng, Ning, Jiao-Lin, Lu, Kai-Zhi, Zhang, Ju, Xia, Zheng-Yuan, Chen, Yu-Wen, Yi, Bin

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A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence by Li, Yu-Jie, Zhang, Li-Ge, Zhi, Hong-Yu, Zhong, Kun-Hua, He, Wen-Quan, Chen, Yang, Yang, Zhi-Yong, Chen, Lin, Bai, Xue-Hong, Qin, Xiao-Lin, Li, Dan-Feng, Wang, Dan-Dan, Gu, Jian-Teng, Ning, Jiao-Lin, Lu, Kai-Zhi, Zhang, Ju, Xia, Zheng-Yuan, Chen, Yu-Wen, Yi, Bin

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