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

Chinese Medicine

Qi Dai¹  Jinchao Wei²  Yan Xue²  Lele Yang²  Peng Li² 

[1] Chengdu Institute for Food and Drug Control, Chengdu, China;State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China;

关键词: Jinqi Jiangtang;    Backpropagation artificial neural network;    Machine learning;    Q-markers;    Mass spectrometry;    Metabolomics;   

DOI  :  10.1186/s13020-021-00438-x

来源: Springer

【 摘 要 】

BackgroundJinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little research has been done to identify and classify its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation.MethodsThis strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishment of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled by using the untargeted UPLC-LTQ-Orbitrap metabolomic approach. The chemical features obtained which were associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV).ResultsOptimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of partial least square (PLS) model (R2 < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. With this machine learning model, 10 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted.ConclusionsThis proposed artificial intelligence approach is desirable for quick and easy identification of Q-markers with bioactivity from JQJT preparation.

【 授权许可】

CC BY