Integrating metabolomic data with machine learning approach for discovery of Q-markers from Jinqi Jiangtang preparation against type 2 diabetes

被引:0
|
作者
Lele Yang
Yan Xue
Jinchao Wei
Qi Dai
Peng Li
机构
[1] University of Macau,State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences
[2] Chengdu Institute for Food and Drug Control,undefined
来源
关键词
Jinqi Jiangtang; Backpropagation artificial neural network; Machine learning; Q-markers; Mass spectrometry; Metabolomics;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 13 条
  • [1] Integrating metabolomic data with machine learning approach for discovery of Q-markers from Jinqi Jiangtang preparation against type 2 diabetes
    Yang, Lele
    Xue, Yan
    Wei, Jinchao
    Dai, Qi
    Li, Peng
    CHINESE MEDICINE, 2021, 16 (01)
  • [2] Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence
    Leiherer, Andreas
    Muendlein, Axel
    Mink, Sylvia
    Mader, Arthur
    Saely, Christoph H.
    Festa, Andreas
    Fraunberger, Peter
    Drexel, Heinz
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2024, 25 (10)
  • [3] A Machine-Learning Approach on Metabolomic Data to Predict Type 2 Diabetes Mellitus Incidence
    Leiherer, Andreas
    Muendlein, Axel
    Saely, Christoph H.
    Plattner, Thomas
    Larcher, Barbara
    Mader, Arthur
    Vonbank, Alexander
    Laaksonen, Reijo
    Fraunberger, Peter
    Drexel, Heinz
    DIABETES, 2024, 73
  • [4] Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach
    Lv, Xiang
    Luo, Jiesi
    Huang, Wei
    Guo, Hui
    Bai, Xue
    Yan, Pijun
    Jiang, Zongzhe
    Zhang, Yonglin
    Jing, Runyu
    Chen, Qi
    Li, Menglong
    FRONTIERS IN ENDOCRINOLOGY, 2024, 15
  • [5] Machine-learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study
    Karmand, Hanieh
    Andishgar, Aref
    Tabrizi, Reza
    Sadeghi, Alireza
    Pezeshki, Babak
    Ravankhah, Mahdi
    Taherifard, Erfan
    Ahmadizar, Fariba
    ENDOCRINOLOGY DIABETES & METABOLISM, 2024, 7 (02)
  • [6] Predicting Rapid Decline in Kidney Function Among Type 2 Diabetes Patients from Laboratory Tests: A Machine Learning Approach
    Nakahara, Eri
    Waki, Kayo
    Kurasawa, Hisashi
    Mimura, Imari
    Seki, Tomohisa
    Fujino, Akinori
    Shiomi, Nagisa
    Nangaku, Masaomi
    Ohe, Kazuhiro
    SSRN,
  • [7] Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach
    Thomsen, Helene B.
    Jakobsen, Mike M.
    Hecht-Pedersen, Nikolaj
    Jensen, Morten Hasselstrom
    Kronborg, Thomas
    JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, 2023,
  • [8] Surrogate markers of insulin resistance and coronary artery disease in type 2 diabetes: U-shaped TyG association and insights from machine learning integration
    Amirhossein Yadegar
    Fatemeh Mohammadi
    Kiana Seifouri
    Kiavash Mokhtarpour
    Sepideh Yadegar
    Ehsan Bahrami Hazaveh
    Seyed Arsalan Seyedi
    Soghra Rabizadeh
    Alireza Esteghamati
    Manouchehr Nakhjavani
    Lipids in Health and Disease, 24 (1)
  • [9] Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait
    Farran, Bassam
    AlWotayan, Rihab
    Alkandari, Hessa
    Al-Abdulrazzaq, Delia
    Channanath, Arshad
    Thanaraj, Thangavel Alphonse
    FRONTIERS IN ENDOCRINOLOGY, 2019, 10
  • [10] Some patients with type 2 diabetes may benefit from intensive glycaemic and blood pressure control: A post-hoc machine learning analysis of ACCORD trial data
    Jiao, Tianze
    Kianmehr, Hamed
    Lin, Yilu
    Li, Piaopiao
    Ospina, Naykky Singh
    Ghayee, Hans K.
    Ruzieh, Mohammed
    Fonseca, Vivian
    Shi, Lizheng
    Zhang, Ping
    Shao, Hui
    DIABETES OBESITY & METABOLISM, 2024, 26 (04): : 1502 - 1509