| Autor: |
Lele Yang, Yan Xue, Jinchao Wei, Qi Dai, Peng Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Chinese Medicine, Vol 16, Iss 1, Pp 1-12 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1749-8546 |
| DOI: |
10.1186/s13020-021-00438-x |
| Popis: |
Abstract Background Jinqi 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. Methods This 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). Results Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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