硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Prediction of the taxonomical classification of the Ranunculaceae family using a machine learning method
发表刊物：New Journal of Chemistry
刊物所在地：ENGLAND
摘要：Ranunculaceae is a botanical source for various pharmaceutically active compounds, which has been commonly utilized in traditional Chinese medicine. Increasing interest in Ranunculaceae pharmaceutical resources has led to a taxonomical study of this family, which might provide new insight to understand its diversification, relationship and phylogenetic position, and further to find new medicinal resources and promising compounds. In this study, we used the machine learning method to explore the classification of the medicinal Ranunculaceae family. 204 species representing 17 genera of the Ranunculaceae family were collected from the TCMID with their 1280 active compounds composed of structure-based fingerprints. After the construction of species-compound and genus-compound matrices, CNNs and Ext fingerprints were determined as the best machine learning method and fingerprint type using ACC and F-score as clustering criteria, respectively. We found that taxonomical classification within the Ranunculaceae family could be accurately predicted, especially at the genus level with a top ACC of 0.86 and an F-score of 0.85. The top features of compounds that were important for the classification of 17 genera were also identified, and thus some genera with high medicinal values were associated with characteristic cis and (or) trans features. As far as we know, this is the first time that some genera are found to be associated with the structural features of compounds.
合写作者：Wenlu Yang,Guodong Tan,Chunyao Tian
第一作者：Jiao Chen
论文类型：SCI
通讯作者：Hongjun Wang,Jiayu Zhou,Hai Liao
文献类型：J
卷号：46
期号：11
页面范围：5150-5161
ISSN号：1144-0546
是否译文：否
收录刊物：SCI