Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Abstract

Authors

• Jiang Li
• Yuefeng Yu
  Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Ying Sun
  CFAR and I2R, Agency for Science, Technology and Research, Singapore.
• Yanqi Fu
  Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Wenqi Shen
  Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Lingli Cai
  Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Xiao Tan
  College of Food Science and Engineering, Northwest University, Xi'an 710069, China; School of Chemistry & Chemical Engineering, Yulin University, Yulin 719000, China.
• Yan Cai
  School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.
• Ningjian Wang
  Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Yingli Lu
  Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, M4N 3M5, Canada.
• Bin Wang
  State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.

Keywords

External Resources