Methodology An improved association-mining

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Methodology

An improved association-mining research for exploring Chinese herbal property theory: based on data of the Shennong’s Classic of Materia Medica Rui Jin1,2, Zhi-jian Lin2, Chun-miao Xue2, Bing Zhang2

1. Department of Pharmacy, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China 2. School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China

ABSTRACT: Knowledge Discovery in Databases is gaining attention and raising new hopes for traditional Chinese medicine (TCM) researchers. It is a useful tool in understanding and deciphering TCM theories. Aiming for a better understanding of Chinese herbal property theory (CHPT), this paper performed an improved association rule learning to analyze semistructured text in the book entitled Shennong’s Classic of Materia Medica. The text was firstly annotated and transformed to well-structured multidimensional data. Subsequently, an Apriori algorithm was employed for producing association rules after the sensitivity analysis of parameters. From the confirmed 120 resulting rules that described the intrinsic relationships between herbal property (qi, flavor and their combinations) and herbal efficacy, two novel fundamental principles underlying CHPT were acquired and further elucidated: (1) the many-to-one mapping of herbal efficacy to herbal property; (2) the nonrandom overlap between the related efficacy of qi and flavor. This work provided an innovative knowledge about CHPT, which would be helpful for its modern research. KEYWORDS: traditional Chinese medicine; Chinese herbal property theory; association rule learning; knowledge discovery; data mining DOI: 10.3736/jintegrmed2013051 Jin R, Lin ZJ, Xue CM, Zhang B. An improved association-mining research for exploring Chinese herbal property: based on data of the Shennong’s Classic of Materia Medica. J Integr Med. 2013; 11(5): 352365.
 Received December 28, 2012; accepted April 15, 2013. Open-access article copyright © 2013 Rui Jin et al. Correspondence: Prof. Bing Zhang; Tel: +86-10-84738606; E-mail: [email protected]

1 Introduction Knowledge discovery in databases (KDD), as a relatively young and interdisciplinary field of computer science, has been adopted by many researchers in traditional Chinese medicine (TCM) in recent decades[1-3]. It is a useful tool for extracting underlying patterns from TCM datasets and translating this traditional medical system into scientific language. Increasingly, basic therapeutic principles from TCM have been explored and explained using KDD techniques. These advances feature in the modernization of the oriental medical system [4]. For example, Zhang et al[5] proposed a latent tree approach to study ZHENG differentiation with application to the kidney deficiency September 2013, Vol.11, No.5

dataset. Ehrman et al[6] employed a random forest approach to analyze the efficacy classification of 8 411 phytochemical compounds from 240 Chinese herbs. Further, two novel networkbased methods (distance-based mutual information model and network target-based identification of multicomponent synergy) were proposed to uncover the combination rules of TCM formulae by the team of Shao Li[7,8], who tried to understand TCM principles in the view of bioinformatics and networks. In fact, the KDD approach has already been considered to be a good tool for mining the therapeutic principles under the mysterious veil of TCM medical theories[1,9]. As the bridge between TCM diagnosis and treatment, Chinese herbal property theory (CHPT) is among the most important but unclear parts of TCM. CHPT incorporates philosophies and terminologies from Chinese meteorology

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and sociology into the TCM concepts such as “cold” and “hot”. This terminology reflects a kind of special observation on therapeutic effects or side effects of medicinal herbs based on the sensations of humanity, within the cultural history of Chinese medicinal practice. According to the records in the earliest extant classic of TCM pharmacology, Shennong’s Classic of Materia Medica (SCMM), CHPT provided the initial core concepts of herbs having specific qi and flavor properties[10,11]. Reflecting the thermal response of herbal treatment, five different types of herbal qi were defined: cold, cool, neutral, warm and hot. Similarly, five herbal flavors, relating to taste sensation, were defined as pungent, sweet, sour, bitter and salty. Concentrating on herbal qi and flavor, CHPT has developed into an expansive theoretical framework that has guided the identification, preparation and clinical use of herbal medicines. It is also most focused on refining the efficacy of herbal treatment (named herbal efficacy), which indicates the capacity of a medicinal herb for therapeutic or toxic effects. While CHPT has been used and explored by TCM practitioners for thousands of years, it is an evolving philosophy that has relied on accumulation of practical and cultural experience throughout its history. Along with TCM modernization, recent interests are focused on CHPT and its scientific explanations. Since 2005, two large research programs specializing in CHPT were approved by the Chinese government and supported by the National Basic Research Program (China 973 Program)[12,13]. Some research focused on searching for the matching therapeutic effects and biomedical markers to a particular herbal property. For example, researchers working in a wide range of animal models have reported observing differences between hot and cold qi in response to smallscale herbal interventions (i.e., behavioral response in mice [14], shifts in cultured neural cell function [15], the growth-thermogram curve of Escherichia coli[16], and the characteristics of neuroendocrine-immune network[17]). However, defining a global relationship between herbal property and curative effects is an ongoing problem, which is noticed by only a few researchers. Thereinto, Xiao et al[18] and Zhou et al[19] reported the relationship between herbal property (qi, flavor and channel tropism) and clinical function based on a data-mining method and a characterization-modeling method respectively. They collected many verb-object phrases in TCM as theoretical “function terms” but performed few analysis on the resulting relations. Therefore, the fundamental principles of CHPT buried in these datasets are still undescribed and require further attention. In our previous work, we collected information from SCMM and mined some rules of association, following standard procedures [20]. After incorporating more data into the SCMM dataset, we have been able to expand and Journal of Integrative Medicine

improve our data-mining experiment by conducting a thorough parameter analysis and translating the mining results into two fundamental principles. These improvements lay on the following: (1) Data structure analysis: the semistructured characteristics of text in SCMM were analyzed. It is helpful for understanding CHPT. (2) Further data integration: the synonyms of TCM terminologies were further integrated into a unified statement. (3) Experimental parameter analysis: two familiar parameters named support and confidence of association rules were analyzed before its determination. Another parameter named lift was also introduced for evaluating the resulting rules. (4) Validation of the results: the resulting strong rules were compared to the TCM theory for validation. (5) Analysis of mining results: the results were translated into fundamental rules underlying CHPT, which should be of benefit for its understanding. The rest of the paper is organized as follows. Section 2 proposes the concept of semistructured data in SCMM, describes the process of structured data extraction and multidimensional table construction to allow the data to be clearly arranged and easily manipulated. Section 3 presents the association rule mining experiment with method introductions, parameter analysis, results and validations. Section 4 contains an elucidation of fundamental principles learned from the results, and Section 5 provides a discussion of these fundamental principles. 2 Data extraction from semistructured text 2.1 Analysis of data structures As a great classic of TCM pharmacology, SCMM collects 365 types of Chinese herbal medicines (including medicinal plants, animals and minerals). The text of the book describes medicinal names, origins, properties (qi and flavor) and efficacy. More than 170 kinds of diseases which belong to internal medicine, surgery, gynecology and pediatrics were also discussed in SCMM[21]. Most of the recorded herbs are still commonly used, such as Mahuang (Ephedrae herba), Rougui (Cinnamomi cortex), Chaihu (Bupleuri radix), Huangqin (Scutellariae radix), Huanglian (Coptidis rhizoma), Qinghao (Artemisiae annuae herba), Dahuang (Rhei radix et rhizoma), Fuzi (Aconiti lateralis radix praeparata) and Renshen (Ginseng radix et rhizoma). Their efficacy has been proved by the long-term clinical practice and in some cases, modern scientific research[22]. In the terms of data organization, the text in SCMM can be defined as semistructured data because it contains mixed sentences and semantic markers[2,23]. On one hand, herbal names can be used for dividing the whole text into 365 herb records; some semantic elements including

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Chinese words “Wei” (followed by herbal flavor), “Zhu” (followed by herbal efficacy), “Yi-ming” (followed by alias) and “Sheng” (followed by place of origin) can be used to identify distinct aspects of contents in each herbal record. That is to say, each herbal record in SCMM may be written in accordance with the format which would be divided into six sequential parts including herbal name, herbal flavor, herbal qi, herbal efficacy, alias and place of origin. On the other hand, efficacy information of herbs is practical-oriented data with features of text-heavy and full of TCM synonyms, lacking a definite structure. It needs to be annotated with specialized data cleaning and integration. Figure 1 presents an analysis on the data structure of ginseng in SCMM, showing all the six content parts. In this paper, the first four parts were selected to construct a table for data management and further mining work. As the model shown in Table 1, each row matches to a single herbal medicine and each column represents a separate piece of herbal information. It describes the main framework of the text information in SCMM, which contains a unique identifier (Herb ID), herbal name, herbal qi, herbal flavor and herbal efficacy.

Figure 1 The semistructured text of ginseng in the book entitled Shennong’s Classic of Materia Medica

This semistructured record was divided by the underlined semantic elements into six parts: ① herbal name, ② herbal flavor, ③ herbal qi, ④ herbal efficacy, ⑤ alias and ⑥ place of origin.

2.2 Construction of multidimensional table Further, by splitting these characteristics into limited field categories, the last three fields of the Table 1 can be considered as three separate sets (dimensions) of descriptive attributes belonging to herbal medicines. A particular combination of candidate attributes in three dimensions can be used for herbal location. One attribute of qi dimension, one attribute of flavor dimension and several attributes of efficacy dimension would serve as markers

of a single herbal medicine. To transform the unstructured text describing efficacy information into structured data, we completed a specialized data preprocess for settling all candidate attributes and ensuring the accuracy and consistency in Chinese vocabulary explanations. Ancient and present-day reference books including the Treatise on the Pathogenesis and Manifestations of All Diseases[24], Internal Medicine of TCM[25], Surgery of TCM[26], Obstetrics and Gynecology of TCM[27] and two proofreading and annotation books for SCMM[28,29] were employed to identify the synonyms of efficacy terminologies, which were integrated into a unified statement (Table 2). After that, the information of the resulting table was as follows: (1) Records: all 365 herbal medicines were contained with 6 of them missing herbal qi and/or flavor. (2) Qi dimension: 5 attributes were identified, including cold, cool, neutral, warm and hot. (3) Flavor dimension: 5 attributes were identified, including pungent, sweet, sour, bitter and salty. (4) Efficacy dimension: 182 attributes were identified, including tonifying the middle qi, clearing away heat, improving vision, relieving cough with dyspnea, curing aggregation-accumulation, treating sore and ulcer, etc. Once the attributes in three dimensions were defined, a three-dimensional table (Table 3) was constructed in an Microsoft Excel file format. Medicinal herbs were located in the table using Boolean values (0, 1). A value of 0 means that the medicine did not have the corresponding attribute, and 1 means that it had. Using ginseng in Figure 1 as an example, the value of the cell identified by the row of ginseng and the column (attribute) of cool was 1 while other values in herbal qi dimension were 0 because the herbal qi of ginseng was cool. 3 Association rule mining experiment 3.1 Data-mining methods Focusing on the relationships between herbal property and efficacy, the interdimensional association rules instead of intradimensional ones were mined through the entire database in this work. That is to say, this method extracted sets of items in the efficacy dimension that often occurred in the herbal medicines containing the particular qi or flavor attribute. A formal statement of the problem was described as follows.

Table 1 Data model of the book entitled Shennong’s Classic of Materia Medica Herb ID

Herbal name

43

Renshen (Ginseng radix et rhizoma)

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Herbal qi Cool

Herbal flavor

Herbal efficacy

Sweet

Tonifying the middle qi, nourishing essence-spirit, settling soul and spirit, tranquilizing, removing pathogenic qi, improving vision, enhancing the wisdom, and promoting longevity

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Table 2 Data integration solutions Unified name

Synonyms in Chinese

Unified name

轻身 / 延年 / 不老 / 神仙 ( 定 / 强 ) 安魂魄

Nourishing essence-spirit Tranquilizing

Harmonizing five Zang

安 ( 定 / 和 ) 五脏

Preventing from pathogenic qi 辟 ( 邪 ) 恶气 ( 不祥 ) Enhancing the wisdom 益(增)智(慧) Strengthening muscles and 坚骨齿 ( 强骨节 )/ 坚 ( 强 ) 筋骨 bones Curing convulsive disease 痉 / 瘈疭 / 项背强急 Clearing away heat 除热 / 主身 ( 大 / 暴 ) 热 / 热气

Nourishing brain marrow ( 强 / 填 ) 补髓 ( 脑 ) Relieving heat vexation and 烦热 / 烦满 / 大烦 fullness Tonifying the middle qi 补中 / 补五脏 / 补内 / 益中气 Curing wind stroke 偏枯 / 中风 / 卒中

a

Synonyms in Chinese

Promoting longevity Settling soul and spirit

a

Tonifying qi

益 气 ( 力 )/ 益 精 气 / 益 ( 脾 / Treating fright palpitation 肾)气

Curing malaria

温疟 / 痎疟 / 疟

Curing impediment disease

( 寒 / 风 / 湿 / 周 / 血 ) 痹 / 痹 Curing consumptive disease 气 / 四肢拘挛 / 机关缓急 / 屈 due to overexertion 伸不利 / 胫重酸痛 / 骨节痛 / 膝痛

Curing flaccidity disease

痿躄 / 四肢偏痿 / 四肢重弱

Curing epilepsy

Activating joint

( 安 )b 养 ( 精 ) 神 / 安心 止 ( 定 ) 惊悸 ( 气 )

惊痫 / 惊 ( 悸 ) 癫痫 / 痫 虚劳 / 劳极 / 羸瘦 / 五劳七伤

通(利)百(关)节

Unblocking the blood meridian 通 ( 保 ) 血脉 / 逐血 / 通血气

Relieving headache and dizziness 风头脑动 / 头眩痛 / 风入脑户

Improving complexion

面生光 / 润泽 / 和颜色 / 媚好

Curing throat impediment

Relieving cough with dyspnea

欬 ( 咳 ) 逆上气 /( 胸胁 ) 逆气

Eliminating flooding and spotting 下血 / 漏下 / 崩中

Removing water retention

( 除 / 逐 / 下 ) 消 ( 腹 中 ) 水 Resolving hard mass in stomach 荡胃中积聚 / 涤积聚饮食 / 逐 and intestine 六腑积聚 ( 气 )/ 肢体浮肿

Curing infertility

绝子 / 无子 / 不孕 / 令人有子

Curing constipation

胃胀闭 / 厌谷胃闭 ( 痹 )

Promoting digestion

消 ( 化 / 利 ) 食 ( 水谷 )

Curing diarrhea

泄澼 / 肠 ( 泄 ) 澼 / 泄痢

Curing dysentery

下痢脓血 / 下痢赤白

Curing vaginal discharge

漏下赤白 / 白沃 / 带下赤白

Curing strangury disease

淋 / 气癃闭 / 溺不下 / 小便余 Curing pudendal sore 沥 / 膀胱热

Expelling and killing worms

杀三虫 / 去长虫 / 去白虫

Treating sore and ulcer

痈 / 痈肿 / 疽 / 疡 / 伤热火烂 / Treating polyp 赤熛 / 浸淫疮

Treating blood amassment

瘀 ( 留 / 止 ) 血 / 恶血

Curing aggregation-accumulation 癥瘕积聚 / 留固结癖 / 坚瘕 / 癥坚 ( 痞 )/ 血瘕 ( 积 )

Curing goiter and tumor

瘿瘤 / 瘰疬 / 颈下核 / 鼠瘘

Curing scabies

Removing toxicity

解 ( 诸 ) 毒 / 鉤 吻 / 鸩 羽 / 蛇 Treating strange diseases caused 蛊毒 / 精魅邪鬼 / 魇寐寤 by ghost 螫 / 蜂 / 猘狗 / 菜 / 肉 / 虫毒

Treating unhealed sore

喉痹 / 咽喉肿痛

阴蚀 / 阴疮 / 阴中肿痛 恶疮 / 久败疮 恶肉 / 死肌 / 息肉

疥瘙 ( 癣 )/ 痂疥

The symbol “/” separate the synonyms. b The words in bracket appear at times.

Let Q={q1, q2, ..., q5} be the set of items (attributes) in qi dimension, F={f1, f2, ..., f5} be the set of items in flavor dimension, and E={e1, e2, ..., e182} be the set of items in efficacy dimension. Let T={t1, t2, ..., t5} be the set of transactions (herbal medicines), where each transaction ti contains a nonempty subset of items chosen from Q, F and E. A transaction tj is said to contain an itemset X of items in Q if X tj. Thus, an interdimensional association rule is an implication of the form X→Y, where X Q, Y E Journal of Integrative Medicine

(or X F, Y E, etc.) and X∩Y=ø. The left hand side of the rule is called the antecedent and the right hand side is called the consequent. In this work, the itemsets that contained only one item were considered in three dimensions, and the antecedent and consequent of a rule were from different dimensions, so as to simplify the interpreting of the relations of each herbal property and efficacy. Table 4 shows the defined formats of rules in this association analysis.

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0 All attributes in qi and flavor dimension and 11 attributes in efficacy dimension were chosen for display. SCMM: shennong’s Classic of Materia Medica.

0 0

Fubi 365

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No.

0 0

1

1

0

0

0

0

1

0

0

0

0 1

0 0

0 0

0 1

1 0

0 Maxianhao 364

0

…

0

0

0

0

0

…

0 0

… …

1 0

…

0

…

1

…

0

…

0

…

1

… …

0 0

… …

0 0

… …

1 0

…

Shihu

…

0

0

44

…

1

0 0

0 0 0 1 1

0

1

0

0

0 0

0 0

0 0

0 1

0 1

0

Renshen 43

0 Gancao 42

0

1

0

1 1 1

0

0

… …

0 1

…

1 0

0 0 1

… … … … … …

0 0

… …

0 0

… …

1 0

…

Juhua 41

0

… …

…

0

0

…

0

1

0

0

0 0

0 0

0 0

0 1

0 1

0

Dansha

0 Yuquan

2

0

1

0

1 0 1

0

0

1

Table 4 The specified forms of association analysis

1

Pungent Sour Sweet Salt Bitter Cold Cool Neutral Warm Hot

Curing Promoting Removing Tonifying Clearing Improving impediment longevity pathogenic qi qi heat vision disease 1 0 1 0 0 0

Herbal flavor Herbal qi

Herbal name Herb ID

Table 3 Three dimensional table of data in SCMM with Boolean values

Function

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Form

1

{Q(q)→E(e)}

2

{F(f)→E(e)}

Implication

3

The occurrence probability of efficacy e when the herbal property {Q(q)∧F(f)→E(e)} is given

4

{E(e)→Q(q)}

5

{E(e)→F(f)}

6

{E(e)→Q(q)∧F(f)}

The probable herbal property that inferred from the efficacy e

Given the association rule X→Y, its quality is often measured by two parameters, support and confidence[30]. The measure “support” gives the proportion of transactions in the datasets that contain X and Y (Formula 1) and the measure “confidence” gives the proportion of transactions containing Y in those ones that contain X (Formula 2). Further, a measure named lift was also employed in this work to evaluate the correlation between antecedent and consequent of a rule. It is defined as the confidence of a rule divided by the support of the consequent (Formula 3). Their symbols and calculations are as follows: Support:

(1)

Confidence:

(2)

Lift:

(3)

where the symbol σ( ) denotes the number of transactions which contain the particular itemset. Support and confidence are the most common measures related to a rule. Their thresholds are used to control the number and quality of the generated rules. However, some associations among uncorrelated elements can be generated using this “support-confidence” framework. In this case, lift is added to further assess the quality of a rule. A rule with the lift greater than one indicates that the rule predicts a consequent better than random chance. The Apriori algorithm that we utilized in this paper for determining association rules was proposed by Agrawal in 1993, and has been widely employed in biomedical and TCM research[20, 31-33]. 3.2 Parameter analysis Suitable support and confidence thresholds allow researchers to identify strong, well-supported rules among many weak and less predictive rules that may emerge from this kind of analysis. For example, a rule with high support value represents the high frequency of occurrences of the items, which should involve the common efficacy in TCM clinics. A high confidence value means the association described by a rule is predictive of a pattern. However,

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excessively high parameter thresholds would lead to overlooking interesting associations. To optimize parameter thresholds, we performed an association rule-mining on a representative sample data that were comprised of cold attribute in qi dimension and all attributes in efficacy dimension, while varying the threshold levels of support and confidence. Then the sensitivities of these parameters were analyzed. First we discuss the sensitivity of the support parameter. Figure 2(a) shows the number of 1-itemset (with cardinality k=1) in the efficacy dimension, when the minimum support count (minisup) ranged from 1 to 30. As can be seen from the upper solid line, the support values of almost half of the attributes in the efficacy dimension were less than 5, and only 30 attributes had support greater than 30. When candidate 2-itemsets were produced by one of the frequent items {E(e i)|σ(e i)≥minisup} in the efficacy dimension and the most frequent item {Q(cold)} in qi dimension, their number was fewer and decreased rapidly to 2 at the minisup of 30, as represented by the lower solid line (Figure 2(a)). It was such a low occurrence for these itemsets that a high minisup threshold would remove many expected interesting associations. Meanwhile, some items with very low support levels like the large number of attributes that appeared only once in the efficacy dimension may occur simply by chance. Thus, minisup was set at 3, resulting in 67 frequent 2-itemsets. Next, confidence and lift were calculated for 134 association patterns, in the forms of {Q(cold)→E(ei)} and {E(ei)→Q(cold)}, developed from 67 frequent 2-itemsets

(Figure 2(b)). This graph shows the strong positive relationship between the confidence and lift values of these association patterns. The confidence of {E(ei)→Q(cold)} patterns presented a linear increase from less than 20% to 100%. Among these patterns, 16 were strong rules with confidence >45% and lift >1.5. On the contrary, most of {Q(cold)→E(ei)} associations were spread irregularly in the area under the 20% confidence curve. Therefore, we set the minimum confidence percentage (miniconf) at 45% to produce association rules efficiently, which also brought the rule a high lift value. Once the minisup and miniconf were defined, the methods generated all association rules that satisfied the named forms. 3.3 Mining results In this work, the association rule algorithm was implemented in three cases corresponding to the six forms (Table 4). Association rules were extracted with the minisup of 3, the miniconf of 45% and a minimum lift of 1. At these thresholds, 120 rules involving approximately 80 kinds of efficacy were obtained. All of these association rules were for itemsets containing just two items. These rules were unevenly distributed among the parameter space and most of them included particular attributes such as hot-qi, neutral-qi, cold-qi, pungent-flavor, sweet-flavor and bitterflavor. Further, the number of rules that efficacy attributes appeared in the antecedent and the above qi or flavor attributes in the consequent was far greater than the rules with the locations interchanged (Figure 3). Table 5 listed all achieved association rules. All rules were numbered and described with support, confidence and lift values.

Figure 2 The sensitivity of parameters

(a) presented the number of itemsets along with the increasing support threshold and (b) presented the confidence distribution of frequent patterns with the minisup of 3. Journal of Integrative Medicine

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Figure 3 The proportion of association rules of specified forms

The proportion of rules with efficacy in the antecedent was 85.83% while that of efficacy in the consequent was 14.17%.

3.4 Validation of results For a given rule X→Y, the higher the confidence, the more likely it is for Y to be present in the transactions that contain X. Hence for the resulting rules in the forms of {E(e)→Q(q)}, {E(e)→F(f)} and {E(e)→Q(q)∧F(f)}, the higher the confidence, the more likely it was for the efficacy e to be presented in the herbs whose property was q, f or their combination. In these strong association rules, the efficacy e was probably believed to be a reason for judgment of the corresponding property. For example, the results showed that cold qi can be inferred from the efficacy of resolving hard mass in stomach and intestine (see rule No. 29). This suggests that a medicinal herb that can resolve hard mass in stomach and intestine of the human body is more likely to have the property of cold qi. Generally, TCM theory states that cold qi has the actions of descending and purging, and can be used in treating accumulations in stomach and intestine by purgation. Therefore, there was a good match between this association rule and TCM theory. In the present approach, all the rules resulting from our analysis are to be checked for conformity with TCM theory and clinical experience (Table 6). As we can see, more than half rules were successfully classified into proper groups of the actions of matching herbal properties in TCM theory. 4 Principles learned form the results The above resulting rules made a good elaboration on the relationships between herbal property and efficacy, which not only suggested the efficacy meanings corresponding to the particular herbal property, but also helped us to explore the global structure of CHPT. When we revisited these rules in the view of mathematics, some general September 2013, Vol.11, No.5

principles that shared by all herbal property attributes emerged. They were underlying principles with seemingly simple essence but rich manifestations, which were elucidated as follows. 4.1 The many-to-one mapping of herbal efficacy to herbal property As we mentioned in part 3.4, some herbal efficacy attributes are strongly linked to qi and flavor of an herb, whose definition and judgment could rely on these attributes. For example, “resolving hard mass in stomach and intestine” is frequently associated with herbs having cold qi (rule No. 29), and its treatment may call for Dahuang. Clearing away heat is also frequently associated with herbs having the property of cold qi (see rule No. 35), and its treatment may call for Zhizi (Gardeniae fructus). Noticeably, one herbal property would be inferred by more than one kind of efficacy, because many efficacy attributes can be the antecedent of these strong rules with only one herbal property in the consequent. This phenomenon was observed for six herbal properties including cold qi, neutral qi, hot qi, pungent flavor, sweet flavor, and bitter flavor. It was also noted there were no two herbal properties in one dimension sharing the same efficacy attribute. For example, none of the antecedents of the rules with cold qi in the consequent appeared in the rules with neutral qi or hot qi in the consequent. This was also observed for all the above six herbal properties. We defined these phenomena as an abstract principle, namely a many-to-one mapping of herbal efficacy to herbal property. Figure 4 illustrates a five-to-one mapping of herbal efficacy to cold qi with some common medicinal herbs. As we can see, these herbs had distinct ways of “expressing” their cold qi. For example, the cold qi of Zhizi and Cheqianzi (Plantaginis semen) should develop from the efficacy of clearing away heat and regulating the waterways respectively, while the cold qi of Dahuang should develop from both regulating the waterways and resolving hard mass in stomach and intestine. The actions represented by the same “cold qi” were different in herbs with cold property. Furthermore, with the diversity and identity of medicinal herbs considered, we believe the many-to-one mapping described in this paper should be one of the most important frameworks underlying CHPT, which is also mentioned briefly in the work of Yao et al[34]. Actually, this principle can demonstrate the TCM wisdoms on grasping the medicinal value of original herbs. It provided a logical approach to the generality and unification of complex herbal therapeutic effects, that is, to classify the similar herbal effects into a group labeled herbal property. 4.2 The nonrandom overlaps between the meanings of qi and flavor attributes Distinct from formal logic, TCM has developed an approach under the influence of Chinese philosophy, which

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359 Neutral

Relieving headache and dizziness

Curing throat impediment

Promoting digestion

Activating joint

Regulating facial complexion

Relieving cough with dyspnea

Relieving lumbago

Fei Jian

Preventing abortion

Settling soul and spirit

Relieving darkish complexion

Relieving difficulty of evacuating

Showing tolerance of hungry

Curing impotence

Curing convulsive disease

Curing hemorrhoid

Strengthening will

Tonifying the middle qi

Harmonizing five Zang

Unblocking the blood meridian

Nourishing sperm

Stopping bleeding

Nourishing essence-spirit

Promoting longevity

Relieving reddened complexion Resolving hard mass in stomach and intestine Relieving diabetes

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

30

29

Neutral

Warming the middle qi

Cold

Cold

Cold

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Neutral

Hot

Hot

Hot

Hot

Hot

Hot

Hot

Hot

Curing flaccidity disease

3

Hot

Consequent

2

Antecedent

Promoting sweating

1

Rule

Table 5 (to be continued) The resulting association rules Conf.

0.778

0.778

1.000

0.457

0.462

0.500

0.500

0.500

0.500

0.500

0.526

0.526

0.556

0.563

0.593

0.600

0.600

0.667

0.750

0.778

0.889

0.457

0.471

0.500

0.600

0.600

0.611

0.636

0.667

0.750

7

7

4

64

6

3

4

4

8

23

10

10

10

9

16

3

3

6

3

7

8

21

8

4

3

6

11

7

4

3

Supp.

Lift

2.868

2.868

3.687

1.255

1.267

1.372

1.372

1.372

1.372

1.372

1.444

1.444

1.525

1.544

1.626

1.647

1.647

1.830

2.058

2.135

2.439

2.057

2.121

2.253

2.704

2.704

2.754

2.868

3.004

3.380

60

59

58

57

56

55

54

53

52

51

50

49

48

47

46

45

44

43

42

41

40

39

38

37

36

35

34

33

32

31

Rule

Antecedent

Consequent

Cold

Cold

Cold

Cold

Cold

Cold

Cold

Cold

Relieving cough with dyspnea Treating strange diseases caused by ghost Curing diarrhea

Curing malaria

Curing Shan Jia

Relieving headache and dizziness

Treating polyp

Relieving itching

Directing qi downward

Promoting digestion

Improving bright spirit

Warming the middle qi

Preventing from pathogenic qi

Promoting sweating

Conf.

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

Pungent

0.500

0.500

0.500

0.524

0.556

0.556

0.556

0.571

0.571

0.600

0.625

0.636

0.667

0.750

7

13

23

11

5

10

15

4

8

3

10

7

6

3

3

3

0.600 1.000

64

19

5

5

5

7

14

7

7

32

3

3

7

4

Supp.

0.481

0.452

0.455

0.455

Cold Cold

0.455

0.467

0.483

0.500

0.500

0.500

0.600

0.600

0.636

0.667

Cold

Cold Promoting Neutral longevity Curing uterine Cool obstruction Killing animals such as fish and mice Pungent

Regulating the waterways

Curing fractures and sinew injury Relieving blindness due to corneal opacity Curing tympanites

Relieving heat vexation and fullness Cold

Curing strangury disease

Curing diarrhea

Curing wind edema and distention

Clearing away heat

Relieving deafness

Removing Fu Shi

Curing jaundice

Leading to early abortion

Lift

1.862

1.862

1.862

1.951

2.069

2.069

2.069

2.128

2.128

2.235

2.328

2.370

2.483

2.793

3.724

1.502

1.255

1.668

1.676

1.676

1.676

1.721

1.780

1.843

1.843

1.843

2.212

2.212

2.346

2.458

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Sweet Sweet

Relieving wind stroke

Curing dysentery

Showing tolerance of hungry

Stopping bleeding Curing consumptive disease due to overexertion Nourishing muscles

Relieving difficulty of evacuating

Improving hearing

Settling soul and spirit

Fei Jian

Nourishing essence-spirit

Treating menstrual flooding and spotting

Tonifying the middle qi

Nourishing the deficiency

Relieving many kinds of diseases

Nourishing brain marrow

Nourishing qi

Regulating facial complexion

Replacing old things with new things

Nourishing wisdom

Curing jaundice

64

65

66

67

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

68

Curing scabies

63

360 Bitter

Bitter

Bitter

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Sweet

Pungent

Pungent

Pungent

Relieving borborygmus

Pungent

Consequent

62

Antecedent

Curing throat impediment

61

Rule

Table 5 (continuation 1) The resulting association rules Conf.

0.818

0.833

1.000

0.471

0.482

0.500

0.500

0.500

0.500

0.533

0.538

0.556

0.556

0.556

0.600

0.600

0.600

0.667

0.815

1.000

0.458

0.471

0.500

0.500

9

5

3

8

40

4

5

7

23

8

7

5

5

12

3

6

22

4

22

4

11

8

3

5

Supp.

Lift

2.262

2.304

2.765

2.227

2.227

2.310

2.310

2.310

2.310

2.464

2.488

2.567

2.567

2.567

2.772

2.772

2.772

3.080

3.765

4.620

1.707

1.753

1.862

1.862

108

107

106

105

104

103

102

101

100

99

98

97

96

95

94

93

92

91

90

89

88

87

86

85

Rule

Sweet-warm

Sweet-hot

Sweet-hot

Sore-neutral

Warming the middle qi

Pungent-cold

Sweet

Sweet

Sour

Unblocking the obstruction of qi

Curing flaccidity disease

Relieving pain

Treating sore and ulcer

Curing hemorrhoid

Relieving diabetes

Removing water retention

Clearing away heat

Curing wind edema and distention

Checking sweating

Showing good memory

Curing tympanites

Relieving reddened complexion

0.600

Promoting longevity

0.643

Promoting longevity

0.500

0.571

Promoting longevity

Tonifying qi

0.545

Pungent-hot

0.474

Clearing away heat

0.785

Promoting longevity

0.506

0.467

Promoting longevity

Tonifying qi

0.452

0.500

0.500

0.500

0.526

0.556

0.563

0.609

0.643

0.667

0.667

0.727

0.750

0.778

0.800

Conf.

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Bitter

Resolving hard mass in stomach and intestine

Consequent Bitter

Antecedent Curing uterine obstruction

3

7

9

4

6

9

40

62

7

14

3

8

33

10

5

9

39

9

4

4

8

3

7

4

Supp.

Lift

1.564

2.199

1.676

1.490

4.424

2.701

2.227

2.046

1.217

1.249

1.383

1.383

1.383

1.455

1.536

1.555

1.685

1.778

1.843

1.843

2.011

2.074

2.151

2.212

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Antecedent: left hand of a rule; Consequent: right hand of a rule; Conf: confidence degree; Supp: support count. The rules No. 1 to No. 43 presented the antecedent-located efficacy that was associated with the attributes in qi dimension and the rules No. 44 to No. 45 showed the efficacy attributes in the consequent. The rules No. 46 to No. 102 listed associations between efficacy and herbal flavor in the same way. In addition, the left 18 rules that between efficacy and the combinations of qi and flavor were presented in the rules No. 103 to No. 120.

3.318 5 0.455 Bitter-cold Curing tympanites 120 1.955 0.750 Promoting longevity Sweet-cold 114

12

3.982 6 0.545 Bitter-cold Curing jaundice 119 2.607 1.000 Promoting longevity Sweet-cool 113

4

4.056 5 0.556 Bitter-cold Relieving diabetes 118 4.731 0.519 Sweet-neutral Showing tolerance of hungry 112

14

4.867 6 Bitter-cold Resolving hard mass in stomach and intestine 117 2.309 0.525 Tonifying qi Sweet-neutral 111

21

116 2.216 0.850 Promoting longevity Sweet-neutral 110

34

Sweet-cold 115 2.639 3 0.600 Tonifying qi Sweet-warm 109

Rule Lift Supp Conf Consequent Antecedent Rule

Table 5 (continuation 2) The resulting association rules

0.667

1.227 8 0.471 Clearing away heat Bitter-cool

1.984 8 0.500

Supp Conf Consequent Antecedent

Curing cold and heat

Lift

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has lead to the informal logic statements of CHPT [35]. From the perspective of TCM history, the development of herbal qi and flavor may not be independent, as they are both related to yin-yang theory and Wu-xing theory[11]. Thus, it was possible to find the same efficacy attribute that strongly relates to the attribute belonging to qi dimension and flavor dimension. However, some efficacy attributes related to one herbal qi did appear in associations with the particular flavor, which made this uncommon. For example, the antecedents of pungent flavor-related rules ({E(e)→F(pungent)}; see rules No.47, No.49, No.51, No.55, No.58 and No.61) were similar to the antecedents of the hot qi-related rules ({E(e)→Q(hot)}, see the rules No.1, No.3, No.4, No.5, No.6 and No.9). These were commonly seen in TCM with high support degrees, which included promoting sweating, warming the middle qi, clearing the throat, promoting digestion, relieving headache and dizziness, and relieving cough with dyspnea. These 6 efficacy attributes strongly point to herbs with hot qi and pungent flavor. In the present work, we identify this association as a “hot-pungent bond”. Following the same approach, we also identified a “neutral-sweet bond” and a “cold-bitter bond” from the resulting rules (Table 7). Of the 25 possible combinations of qi and flavor, only 3 kinds of strong “bonds” were identified with the present approach. These relationships appear to identify nonrandom connections between the two herbal properties of qi and flavor. According to this finding, the medicinal herbs that can be viewed as combinations of qi and flavor should be naturally classified into two theoretical groups. The first group was composed of herbs following this kind of strong “bond” (hot-pungent, neutral-sweet and cold-bitter), where the related efficacy attributes of qi and flavor were similar. Here, we refer to them as the “isotropic” herbs. The second group was composed of the herbs with attributes that were not in a strong “bond”, where the related efficacy attributes were different such as hot-sweet, neutral-bitter, cold-pungent, etc. Here we refer to this group as “anisotropic” herbs. As shown in Figure 5, “isotropic” herbs, “anisotropic” herbs and their relationships can be explained by using network method. We believe that an “isotropic” herb might have a synergistic and amplified effect while an “anisotropic” herb might be divergent and diverse. The latter should be paid more attention in clinical applications due to the diversity of their individual therapeutic effects, especially the potential compatibility prospective in a TCM formula. 5 Discussion 5.1 Association rule learning method Association rule learning aims to extract interesting common patterns or causal links among sets of items in

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Table 6 The comparison between the resulting rules and traditional Chinese medicine theory Herbal property Hot qi

Actions in traditional Chinese medicine theory Expelling cold; restoring yang Warming and activating qi and blood

Numbers of the related rules 1, and 3 2, 4, 6, 7, and 9

Neutral qi

Nourishing

11, 18, 20, 21, 22, 24, 26, and 27

Harmonizing; mitigating

10, 12, 13, 14, 15, 16, 17, and 25

Clearing away heat; purging fire

30, 32, 35, 39, and 41

Treating disease related to water retention

36, 42, and 43

Descending

29, and 31

Dispersing; dispelling wind pathogens

47, 53, 54, 55, 60, 63, and 64

Promoting the circulation of qi and blood

51, 52, 58, and 62

Treating strange diseases considered to be related to ghost in TCM

48, 50, 56, and 59

Nourishing

68, 69, 73, 74, 76, 77, 79, and 80

Harmonizing; mitigating

66, 67, 70, 71, 72, 75, 78, and 81

Drying; resolving water and dampness pathogen

85, 88, 90, 91, and 93

Purging heat Descending; resolving food stagnation

84, 87, 92, 94, and 96 82, 86, and 100

Cold qi

Pungent flavor

Sweet flavor Bitter flavor

Table 7 The bonds between herbal qi and flavor The bonds Hot-pungent bond (strong)

Efficacy attributes Promoting sweating Warming the middle qi Relieving headache and dizziness Curing throat impediment Promoting digestion Relieving cough with dyspnea

Neutral-sweet bond (strong) Fei Jian

Figure 4 The many-to-one mapping of herbal efficacy to cold qi

Settling soul and spirit

The cold qi of different herbs may be inferred from different kinds of efficacy. The presented medicinal herbs are Dahuang (Rhei radix et rhizoma), Zhizi (Gardeniae fructus), Cheqianzi (Plantaginis semen), Huangbai (Phellodendri chinensis cortex) and Zhimu (Anemarrhenae rhizoma).

a transaction database, and is among the most frequent KDD methods. It has been successfully used in research projects focused on identifying interesting correlations in complex datasets spanning, market basket analysis, webmining, bioinformatics, and now KDD in TCM. Discovering the frequent combination rules of medicinal herbs from TCM formula data, and analyzing associations between ZHENG and symptoms or biochemical indicators of patients from medical database were the most common types of association rule learning studies in TCM[36-38]. In this work, we applied this method to CHPT research and identified several nonrandom underlying associations. In the association rule-mining method, the final rules are identified under the control of several parameters, namely support and confidence. It is believed that the proper parameterization of the method ensures that the resulting rules are strongly predictive of associations in the dataset. For the above reason, in the present study, we implemented September 2013, Vol.11, No.5

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Relieving difficulty of evacuating Showing tolerance of hungry Tonifying the middle qi Stopping bleeding Nourishing essence-spirit Cold-bitter bond (strong)

Relieving reddened complexion Resolving hard mass in stomach and intestine Relieving diabetes Curing jaundice Clearing away heat Curing wind edema and distention Curing tympanites

Hot-sweet bond (weak)

Regulating facial complexion

Hot-bitter bond (weak)

Curing flaccidity disease

Neutral-bitter bond (weak)

Curing hemorrhoid

Cold-pungent bond (weak)

Curing diarrhea Journal of Integrative Medicine

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Figure 5 The example of “isotropic” herbs, “anisotropic” herbs and their relationships The strongly related efficacy attributes (in rectangles) are marked next to the corresponding herbal property (in ovals). The herbs (in circles) are connected with its efficacy by straight lines. The “isotropic” herbs are represented by the red, green and blue circles. They are p u n g e n t - h o t h e r b s Wu z h u y u (WZY, Euodiae fructus), Fuzi (FZ, Aconiti lateralis radix praeparata) and Xixin (XX, Asari radix et rhizoma); sweet-neutral herbs Puhuang (PH, Typhae pollen) and E-jiao (EJ, Asini corii colla); and bitter-cold herbs Dahuang (DaH, Rhei radix et rhizoma) and Zhizi (ZZ, Gardeniae fructus). The “anisotropic” herbs are represented by the purple circles. They are sweet-hot herb Shizhongru (SZR, Stalactitum), pungent-cold herb Ningshuishi (NSS, Calcitum seu Gypsum rubrum), bitter-hot herbs Mahuang (MH, Ephedrae herba) and Baizhu (BZ, Atractylodis macrocephalae rhizoma), sweetcold herbs Maogen (MG, Imperatae rhizoma) and Dihuang (DiH, Rehmanniae radix) and bitter-neutral herb Chaihu (CH, Bupleuri radix).

a global, complete and detailed parameter analysis before the major mining work. The parameter called “lift” was added in the present study to cross-check the quality of rules. Incorporation of these two new processes improved the current findings over results from our previous work. 5.2 The SCMM SCMM, dating from the Han Dynasty (202 B.C. to 220 A.D.), is considered the earliest extant classic TCM pharmacology text. By recording the reliable effective medicinal herbs, proposing herbal classification methods, describing herbal property and compatibility theory, and discussing herbal efficacy with the place of origin and the preparation, etc[39], the SCMM establishes the fundamentals of the contemporary Chinese materia medica[11]. Many herbs initially recorded in SCMM are still in use and have reliable clinical results. Further, the recording style and the organization of herbal property theory in SCMM established the model for TCM pharmacology books that were written over the following 2 000 years, including Tang Mateira Medica, Classified Emergency Materia Medica, and Compendium of Materia Medica [40] . Therefore, Journal of Integrative Medicine

SCMM should be the appropriate material for studying CHPT. Even though the mining results might be somewhat different depending on the reference text used for building the base dataset, the final rules in the present study did uncover several nonrandom associative rules that reflect the inherent structure and complexity at the core of CHPT. 5.3 About future research As TCM has been modernized throughout the last century, a number of researchers have been attracted to investigating the scientific explanations of CHPT. They usually seek the various types of chemical or biochemical indicators that underlie herbal properties. However, the results of these research efforts do not replace the understanding of the fundamental rules underlying CHPT, which should be one crucial aspect of the CHPT modernization. The two principles we learned from the association rules in this paper, begin to fill this gap. The many-to-one mapping of herbal efficacy to herbal properties (qi and flavor) reveals that although properties are shared among herbs they are not predictive of clinical herb use. Thus, herbal properties describe a classification of action, not a specific outcome.

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This difference emphasizes the individuality of herbs, and implies that simply pursuing the chemical constituents or biochemical indicators that confer these herbal properties may be an unreasonable approach. In other words, the use of Western disease-specific targets was not a reasonable approach to evaluate TCM [41]. Second, the nonrandom overlap between the efficacy meanings of qi and flavor attributes demonstrated close relations between hot qi and pungent flavor, neutral qi and sweet flavor, and cold qi and bitter flavor. These patterns not only reflected the inherent relationships between qi and flavor but also gave new insights into explaining their roles. These findings will also provide researchers with a novel perspective to understand the wisdom by which TCM organizes the complexity of herbal medicine with simple ideas.

3 4

5 6 7

6 Conclusions Here we have presented an improved method that extracts meaningful patterns from the data contained within the ancient Chinese book of Materia Medica to understand CHPT. The approach was based on association discovery technologies with a specialized annotation of ancient Chinese vocabularies and a proper parameter analysis. Finally we identified 120 association rules of six defined formats, including the relations between herbal efficacy and qi, flavor and their combinations. Different from other computational approaches to CHPT, our work aimed at exploring the global fundamental principles embedded in CHPT from these association rules. This process identified the many-to-one mapping of herbal efficacy to herbal property and the nonrandom overlaps between the meanings of qi and flavor attributes. Understanding these principles will support the modernization of TCM philosophy, especially in the context of integrative medicine and rational clinical herb use. In this sense, our findings, and similar attempts to link TCM theory and clinical practice will be of great use to both practitioners and researchers alike.

8 9 10 11 12

13

14

7 Funding and acknowledgements We thank for the support of National Basic Research Program of China (973 Program) (No. 2007CB512605) and the Scientific Research Innovation Team of Beijing University of Chinese Medicine (No. 2011-CXTD-14).

15

16

8 Conflict of interests The authors declare that they have no conflict of interests. REFERENCES 1 2

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types include reviews, systematic reviews and meta-analyses, randomized controlled and pragmatic trials, translational and patient-centered effectiveness outcome studies, case series and reports, clinical trial protocols, preclinical and basic science studies, papers on methodology and CAM history or education, editorials, global views, commentaries, short communications, book reviews, conference proceedings, and letters to the editor.

● Quick decision and online first publication

For information on manuscript preparation and submission, please visit JIM website. Send your postal address by e-mail to [email protected], we will send you a complimentary print issue upon receipt. Editors-in-Chief: Wei-kang Zhao & Lixing Lao. ISSN 2095-4964. Published by Science Press, China. Journal of Integrative Medicine

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September 2013, Vol.11, No.5