HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning
HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real-world scenarios, eliminating the need for feature ranking and selection in classical machine learning-based methods.
Authors
- Yuyang Sha
- Wenzhi Yang (ORCID: https://orcid.org/0000-0003-2780-4853)
- Meiting Jiang
- Yong-Kang Qiao (ORCID: https://orcid.org/0000-0003-3166-1656)
- Xiaobing Zhai (ORCID: https://orcid.org/0000-0002-4575-7360)
- Hongxin Pan
- Gang Luo (ORCID: https://orcid.org/0000-0003-0623-6236)
- Weiyu Meng
- Yan Yan (ORCID: https://orcid.org/0000-0003-2120-4662)
- Kefeng Li (ORCID: https://orcid.org/0000-0002-7233-4347)
- Junrong Li
Institutions
- Tianjin University of Traditional Chinese Medicine (CN)
- Sun Yat-sen University (CN)
- Beijing Normal University (CN)
- Fifth Affiliated Hospital of Sun Yat-sen University (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- Phytochemical Analysis
- Published
- 2024-08-21
- DOI
- https://doi.org/10.1002/pca.3437
- Citations
- 9
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.10