Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (5): 75-83    DOI: 10.11925/infotech.2096-3467.2019.0974
Current Issue | Archive | Adv Search |
Similarity Measurement of Traditional Chinese Medicine Components for Cold-hot Nature Discrimination
Wei Guohui1,2,Zhang Fengcong1,Fu Xianjun1,Wang Zhenguo1()
1Key Laboratory of Theory of TCM, Ministry of Education of China, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
2School of Science and Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
Download: PDF (809 KB)   HTML ( 12
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper tries to measure the similarity of traditional Chinese medicine components, and then establish a discriminant method for their cold and hot natures.[Methods] Traditional Chinese medicines with similar compositions have similar medicinal properties. Therefore, we used ultraviolet spectra to characterize their components and retrieved the UV spectrum data of 61 traditional Chinese medicines. Then, we used the Mahalanobis distance to measure the similarities of these UV spectrum data. Finally, we constructed a prediction and recognition model for cold and hot natures based on the majority voting algorithm.[Results] We evaluated the proposed model with cross validation and extrapolation techniques. With the solvent of petroleum ether, areas under the ROC curve of cross validation and extrapolated prediction were 0.883 and 0.866. Predictive accuracies of cross validation and extrapolated prediction were 0.754 and 0.776. With multi-solvent comprehensive analysis, the accuracies of cross validation and extrapolation were 0.672 and 0.686.[Limitations] The data size of our study needs to be expanded.[Conclusions] The proposed model could effectively identify ultraviolet spectrum of traditional Chinese medicine components.

Key wordsNature of Chinese Traditional Medicines      Similarity      Mahalanobis Distance      Ultraviolet Spectrum      Predictive Model     
Received: 26 August 2019      Published: 15 June 2020
ZTFLH:  TP391 G354  
Corresponding Authors: Wang Zhenguo     E-mail: zhenguow@126.com

Cite this article:

Wei Guohui,Zhang Fengcong,Fu Xianjun,Wang Zhenguo. Similarity Measurement of Traditional Chinese Medicine Components for Cold-hot Nature Discrimination. Data Analysis and Knowledge Discovery, 2020, 4(5): 75-83.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0974     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I5/75

The UV Absorption Curve of Kochiae Fructus with Petroleum Ether Solvent
序号 中药 序号 中药 序号 中药
1 薄荷(寒) 21 毛知母(寒) 41 附子(热)
2 侧柏(寒) 22 墨旱莲 (寒) 42 甘松(热)
3 柴胡(寒) 23 蒲公英(寒) 43 干姜(热)
4 车前子(寒) 24 秦皮(寒) 44 高良姜(热)
5 川贝母(寒) 25 瞿麦(寒) 45 藁本(热)
6 大黄(寒) 26 生地黄(寒) 46 红花(热)
7 大青叶(寒) 27 天冬(寒) 47 厚朴(热)
8 淡竹叶(寒) 28 豨茜草(寒) 48 麻黄(热)
9 地肤子(寒) 29 栀子(寒) 49 木瓜(热)
10 防己(寒) 30 紫草(寒) 50 木香(热)
11 甘遂(寒) 31 白胡椒(热) 51 羌活(热)
12 葛根(寒) 32 白芥子(热) 52 肉桂(热)
13 瓜蒌(寒) 33 半夏(热) 53 檀香(热)
14 海藻(寒) 34 荜茇(热) 54 天南星(热)
15 黄柏(寒) 35 补骨脂(热) 55 威灵仙(热)
16 黄连(寒) 36 苍术(热) 56 吴茱萸(热)
17 金银花(寒) 37 草豆蔻(热) 57 细辛(热)
18 龙胆(寒) 38 陈皮(热) 58 仙茅(热)
19 芦荟(寒) 39 川芎(热) 59 延胡索(热)
20 络石藤(寒) 40 杜仲(热) 60 淫羊藿(热)
61 炙延胡索(热)
61 Traditional Chinese Medicines
λ and K
">
ACC Values with Different λ and K
The ROC Values of UV Nature Identification with Four Solvents
溶剂 AUC ACC
无水乙醇 0.673 0.510
氯仿 0.759 0.602
蒸馏水 0.752 0.675
石油醚 0.883 0.754
AUC and ACC Values of Chinese Medicine Nature Identification with Four Solvents
分类算法 AUC ACC
人工神经网络 0.663 0.590
支持向量机 0.795 0.738
极限学习机 0.587 0.525
本文模型 0.883 0.754
Stability Comparison of Single Solvent Models
分类算法 ACC
人工神经网络 0.560
支持向量机 0.623
极限学习机 0.506
本文模型 0.672
Stability Comparison of Multiple Solvent Models
分类算法 AUC ACC
人工神经网络 0.650±0.09 0.620±0.01
支持向量机 0.790±0.09 0.695±0.09
极限学习机 0.676±0.09 0.605±0.06
本文模型 0.866±0.03 0.776±0.08
Comparison of Single Solvent Models
分类算法 ACC
人工神经网络 0.590±0.04
支持向量机 0.673±0.09
极限学习机 0.578±0.06
本文模型 0.686±0.06
Comparison of Multiple Solvent Models
真实值

预测值
寒性药 热性药
寒性药 22 8
热性药 7 24
Confusion Matrix of Nature Prediction of 61 Chinese Medicines
指标 寒性药 热性药
召回率 0.733 0.774
精确率 0.758 0.750
F值 0.745 0.762
The Performance of Nature Prediction of 61 Chinese Medicines
[1] Chan K. Progress in Traditional Chinese Medicine[J]. Trends in Pharmacological Sciences, 1995,16(6):182-187.
[2] Ung C Y, Li H, Cao Z W, et al. Are Herb-pairs of Traditional Chinese Medicine Distinguishable from Others? Pattern Analysis and Artificial Intelligence Classification Study of Traditionally Defined Herbal Properties[J]. Journal of Ethnopharmacology, 2007,111(2):371-377.
[3] 欧阳兵, 王振国, 李峰, 等. 中药四性“性-效-物质三元论”假说及其论证[J]. 山东中医药大学学报, 2008,32(3):182-183.
[3] ( Ouyang Bing, Wang Zhenguo, Li Feng, et al. The Hypojournal and Demonstration of the “Tri-element of Property-effect-material” of Four Nature of Traditional Chinese Medicine[J]. Journal of Shandong University of Traditional Chinese Medicine, 2008,32(3):182-183.)
[4] 王振国, 王鹏, 欧阳兵. 中药物质成分与寒热药性相关性研究的方法学框架[J]. 浙江中医药大学学报, 2009,33(5):734-737.
[4] ( Wang Zhenguo, Wang Peng, Ouyang Bing. Methodological Frame of Relativity Research on TCM Substance Component and Drug Nature of Cold and Hot[J]. Journal of Zhejiang University of Traditional Chinese Medicine, 2009,33(5):734-737.)
[5] 魏国辉, 齐守良, 钱唯, 等. 基于相似性度量的肺结节图像检索算法[J]. 东北大学学报:自然科学版, 2018,39(9):1226-1231.
[5] ( Wei Guohui, Qi Shouliang, Qian Wei, et al. Image Retrieval Algorithm of Pulmonary Nodules Based on Similarity Measurement[J]. Journal of Northeastern University:Natural Science, 2018,39(9):1226-1231.)
[6] 杨波, 王振国. 植物类中药寒热药性与无机元素相关性研究[J]. 南京中医药大学学报, 2011,27(2):109-111.
[6] ( Yang Bo, Wang Zhenguo. Literature Study on Relationship Between Cold-Heat Nature of Plant-Based Chinese Herbs and Inorganic Elements[J]. Journal of Nanjing University of Traditional Chinese Medicine, 2011,27(2):109-111.)
[7] 谢欢欢, 陈晨, 王鹏. 基于科属分类下的中药物象-药性相关性研究概述[J]. 中华中医药杂志, 2017,32(8):3620-3622.
[7] ( Xie Huanhuan, Chen Chen, Wang Peng. Study Review of Correlation Between Object Image and Nature of Traditional Chinese Medicine Based on Family and Genus Classification[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2017,32(8):3620-3622.)
[8] 聂斌, 郝竹林, 桂宝, 等. 基于随机森林的中药寒、热药性代谢组学判别方法研究[J]. 江西中医药大学学报, 2015,27(2):82-86.
[8] ( Nie Bin, Hao Zhulin, Gui Bao, et al. The Research for Metabolomics Discriminant Method for Cold and Hot Property of Traditional Chinese Medicine Based on Random Forest[J]. Journal of Jiangxi University of Traditional Chinese Medicine, 2015,27(2):82-86.)
[9] 李雨, 李骁, 薛付忠, 等. 基于人工神经网络的中药药性判别研究[J]. 山东大学学报:医学版, 2011,49(1):57-61.
[9] ( Li Yu, Li Xiao, Xue Fuzhong, et al. Discrimination of Properties of Chinese Traditional Medicines Based on an Artificial Neural Network[J]. Journal of Shandong University:Health Sciences, 2011,49(1):57-61.)
[10] 刘文慧, 李雨, 纪玉佳, 等. 偏最小二乘在中药药性判别中的应用[J]. 山东大学学报:医学版, 2012,50(1):151-154.
[10] ( Liu Wenhui, Li Yu, Ji Yujia, et al. Partial Least Squares in the Discrimination of Traditional Chinese Herbal Medicine Property[J]. Journal of Shandong University: Health Sciences, 2012,50(1):151-154.)
[11] 张新新, 李雨, 纪玉佳, 等. 主成分-线性判别分析在中药药性识别中的应用[J]. 山东大学学报:医学版, 2012,50(1):143-146.
[11] ( Zhang Xinxin, Li Yu, Ji Yujia, et al. Discrimination of Properties of Chinese Traditional Medicine with Principal Component Analysis-linear Discriminant Analysis[J]. Journal of Shandong University: Health Sciences, 2012,50(1):143-146.)
[12] 李和光. 中药寒热药性物质基础紫外光谱指纹图谱数字化表征体系的构建[D]. 济南:山东中医药大学, 2010.
[12] ( Li Heguang. Construction of the Digital Representation System Based on the Ultraviolet Spectrum Fingerprints of the Substantial Basis of Traditional Chinese Medicine (TCM) with Cold-heat Nature[D]. Jinan: Shandong University of Traditional Chinese Medicine, 2010.)
[13] 容蓉, 邱丽丽, 吕青涛, 等. 高效液相色谱法优选四味中药的提取方法[J]. 中成药, 2011,33(4):699-702.
[13] ( Rong Rong, Qiu Lili, Lv Qingtao, et al. A Research on the Optimization Extraction Methods of Four Traditional Chinese Medicines by HPLC[J]. Chinese Traditional Patent Medidicine, 2011,33(4):699-702.)
[14] Long W, Liu P, Xiang J, et al. A Combination System for Prediction of Chinese Materia Medica Properties[J]. Computer Methods and Programs in Biomedicine, 2011,101(3):253-264.
[15] Fu X, Mervin L H, Li X, et al. Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis[J]. Journal of Chemical Information and Modeling, 2017,57(3):468-483.
[16] 王永, 王永东, 郭慧芳, 等. 一种基于离散增量的项目相似性度量方法[J]. 数据分析与知识发现, 2018,2(5):70-76.
[16] ( Wang Yong, Wang Yongdong, Guo Huifang, et al. Measuring Item Similarity Based on Increment of Diversity[J]. Data Analysis and Knowledge Discovery, 2018,2(5):70-76.)
[17] Christensen J H, Mortensen J, Hansen A B, et al. Chromatographic Preprocessing of GC-MS Data for Analysis of Complex Chemical Mixtures[J]. Journal of Chromatography A, 2005,1062(1):113-123.
[18] Wei G, Cao H, Ma H, et al. Content-based Image Retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric[J]. Journal of Medical Systems, 2018, 42: Article No. 13.
[19] 艾科, 马国帅, 杨凯凯, 等. 一种基于集成学习的科研合作者潜力预测分类方法[J]. 计算机研究与发展, 2019,56(7):1383-1395.
[19] ( Ai Ke, Ma Guoshuai, Yang Kaikai, et al. A Classification Method of Scientific Collaborator Potential Prediction Based on Ensemble Learning[J]. Journal of Computer Research and Development, 2019,56(7):1383-1395.)
[20] 刘文慧. 穷举建模策略用于中药药性特征标记的统计模式识别模型研究[D]. 济南: 山东大学, 2012.
[20] ( Liu Wenhui. Research on Statistical Pattern Recognition Model for Chmp-markers Using Exausitive Method[D]. Jinan: Shandong University, 2012.)
[21] Wei G, Ma H, Qian W, et al. A Content-based Image Retrieval Scheme for Lung Nodule Classification[J]. Current Medical Imaging Reviews, 2017,13(2):210-216.
[22] Malar E, Kandaswamy A, Chakravarthy D, et al. A Novel Approach for Detection and Classification of Mammographic Microcalcifications Using Wavelet Analysis and Extreme Learning Machine[J]. Computers in Biology & Medicine, 2012,42(9):898-905.
[1] Han Hui, Liu Xiuwen. Automatic Scoring for Subjective Questions in Maritime Competency Assessment[J]. 数据分析与知识发现, 2021, 5(8): 113-121.
[2] Liu Wenbin, He Yanqing, Wu Zhenfeng, Dong Cheng. Sentence Alignment Method Based on BERT and Multi-similarity Fusion[J]. 数据分析与知识发现, 2021, 5(7): 48-58.
[3] Yan Qiang,Zhang Xiaoyan,Zhou Simin. Extracting Keywords Based on Sememe Similarity[J]. 数据分析与知识发现, 2021, 5(4): 80-89.
[4] Xiang Zhuoyuan,Liu Zhicong,Wu Yu. Adaptive Recommendation Model Based on User Behaviors[J]. 数据分析与知识发现, 2021, 5(4): 103-114.
[5] Lv Xueqiang,Luo Yixiong,Li Jiaquan,You Xindong. Review of Studies on Detecting Chinese Patent Infringements[J]. 数据分析与知识发现, 2021, 5(3): 60-68.
[6] Wu Yanwen, Cai Qiuting, Liu Zhi, Deng Yunze. Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
[7] Sheng Jiaqi, Xu Xin. Expanding Scholar Labels with Research Similarity and Co-authorship Network[J]. 数据分析与知识发现, 2020, 4(8): 75-85.
[8] Xu Yicong,Tian Xuedong,Li Xinfu,Yang Fang,Shi Qingxuan. Retrieving Mathematical Expressions Based on Hesitant Fuzzy Weight[J]. 数据分析与知识发现, 2020, 4(7): 118-126.
[9] Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[10] Liu Ping,Peng Xiaofang. Calculating Word Similarities Based on Formal Concept Analysis[J]. 数据分析与知识发现, 2020, 4(5): 66-74.
[11] Gao Yuan,Shi Yuanlei,Zhang Lei,Cao Tianyi,Feng Jun. Reconstructing Tour Routes Based on Travel Notes[J]. 数据分析与知识发现, 2020, 4(2/3): 165-172.
[12] Han Kangkang,Xu Jianmin,Zhang Bin. Recommending Microblogs with User’s Interests and Multidimensional Trust[J]. 数据分析与知识发现, 2020, 4(12): 95-104.
[13] Li Jiaquan,Li Baoan,You Xindong,Lü Xueqiang. Computing Similarity of Patent Terms Based on Knowledge Graph[J]. 数据分析与知识发现, 2020, 4(10): 104-112.
[14] Yan Yu,Lei Chen,Jinde Jiang,Naixuan Zhao. Measuring Patent Similarity with Word Embedding and Statistical Features[J]. 数据分析与知识发现, 2019, 3(9): 53-59.
[15] Peng Guan,Yuefen Wang,Zhu Fu. Analyzing Topic Semantic Evolution with LDA: Case Study of Lithium Ion Batteries[J]. 数据分析与知识发现, 2019, 3(7): 61-72.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn