A Data Mining-Based Analysis of Medication Rules in Treating Bone Marrow Suppression by Kidney-Tonifying Method

Traditional Chinese medicine use is associated with lower risk of pneumonia in patients with systemic lupus erythematosus: a population-based retrospective cohort study

Objectives: To investigate the association between traditional Chinese medicine (TCM) therapy and the risk of pneumonia in patients with systemic lupus erythematosus (SLE). Methods: This population-based control study analyzed the data retrieved from the National Health Insurance Research database in Taiwan. From a cohort of 2 million records of the 2000-2018 period, 9,714 newly diagnosed patients with SLE were initially included. 532 patients with pneumonia and 532 patients without pneumonia were matched 1:1 based on age, sex, and year of SLE diagnosis using propensity score matching. The use of TCM therapy was considered from the SLE diagnosis date to the index date and the cumulative days of TCM therapy were used to calculate the dose effect. Conditional logistic regression was used to investigate the risk of pneumonia infection. Furthermore, to explore the severity of pneumonia in SLE, sensitivity analyses were performed after stratification using the parameters of emergency room visit, admission time, and antibiotic use. Results: TCM therapy for >60 days could significantly reduce the risk of pneumonia in patients with SLE (95% CI = 0.46-0.91; p = 0.012). Stratified analysis showed that TCM use also reduced the risk of pneumonia in younger and female patients with SLE by 34% and 35%, respectively. TCM for >60 days significantly reduced the risk of pneumonia in the follow-up periods of >2, >3, >7, and >8 years. In addition, the exposure of TCM for >60 days reduced the risk of pneumonia in patients with SLE who were treated with antibiotics for moderate or severe pneumonia. Finally, the study found that using formulae to tonify the kidney for more than 90 days and formulae to activate blood circulation for less than 30 days could significantly reduce the risk of pneumonia infection in patients with SLE. Conclusion: TCM use is associated with a lower risk of pneumonia among patients with SLE.

Withdrawn: A data-mining based analysis of traditional Chinese medicine in diagnosing and treating COVID-19

Yongshen Fan, Ting Zhao, Yaxue Han, Zi Yang, Shan Fang, Mengqing Zhang, Zhijun Xie, A data-mining based analysis of traditional Chinese medicine in diagnosing and treating COVID-19, The Anatomical Record: Advances in Integrative Anatomy and Evolutionary Biology 2021, (https://doi.org/10.1002/ar.24677). The above article, published online on 24 June 2021 in Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the journal Editor-in-Chief, Heather F. Smith, Ph.D., the American Association for Anatomy, and Wiley Periodicals LLC. The authors have been notified of the decision to withdraw the article. The decision is based on concerns raised by a third party. The Editor-in-Chief considers the conclusions of the paper compromised and has initiated an investigation.

Natural products for migraine: Data-mining analyses of Chinese Medicine classical literature

Background: Treatment effect of current pharmacotherapies for migraine is unsatisfying. Discovering new anti-migraine natural products and nutraceuticals from large collections of Chinese medicine classical literature may assist to address this gap.

Methods: We conducted a comprehensive search in the Encyclopedia of Traditional Chinese Medicine (version 5.0) to obtain migraine-related citations, then screened and scored these citations to identify clinical management of migraine using oral herbal medicine in history. Information of formulae, herbs and symptoms were further extracted. After standardisation, these data were analysed using frequency analysis and the Apriori algorithm. Anti-migraine effects and mechanisms of actions of the main herbs and formula were summarised.

Results: Among 614 eligible citations, the most frequently used formula was chuan xiong cha tiao san (CXCTS), and the most frequently used herb was chuan xiong. Dietary medicinal herbs including gan cao, bai zhi, bo he, tian ma and sheng jiang were identified. Strong associations were constructed among the herb ingredients of CXCTS formula. Symptoms of chronic duration and unilateral headache were closely related with herbs of chuan xiong, gan cao, fang feng, qiang huo and cha. Symptoms of vomiting and nausea were specifically related to herbs of sheng jiang and ban xia.

Conclusion: The herb ingredients of CXCTS which presented anti-migraine effects with reliable evidence of anti-migraine actions can be selected as potential drug discovery candidates, while dietary medicinal herbs including sheng jiang, bo he, cha, bai zhi, tian ma, and gan cao can be further explored as nutraceuticals for migraine.

Exploring the Pharmacological Mechanism of the Effective Chinese Medicines Against Gynecological Cancer Based on Meta-Analysis Combined With Network Pharmacology Analysis

This meta-analysis plus network pharmacology aimed to investigate whether traditional Chinese medicine (TCM) combined with chemotherapy is associated with more beneficial efficacy data in the treatment of gynecological cancer (GC). A total of 11 randomized controlled trials (RCTs) consisting of 863 GC patients were included. Results showed a better ORR (RR: 1.42, 95% CI: 1.18–1.71; I² = 21.4%; p = 0.282), DCR (RR: 1.13, 95% CI: 1.03–1.25; I² = 0.0%; p = 0.492), PD (RR: 0.27, 95% CI: 0.11–0.65, p = 0.003; I² = 0.0%, p = 0.930), and QOL (SMD: 0.85, 95% CI: 0.38–1.33, p = 0.005) and higher proportions of CD3⁺ T (WMD: 5.65, 95% CI: 4.23–7.08, p = 0.000; I² = 68.3%, p = 0.004), CD4⁺ T (WMD: 6.97, 95% CI: 5.35–8.59, p = 0.000; I² = 83.4%, p = 0.000), and the CD4⁺/CD8⁺ T ratio (WMD: 0.32, 95% CI: 0.23–0.42, p = 0.000; I² = 78.0%, p = 0.000). The number of adverse events (AEs) was significantly lower in the TCM + chemotherapy group. The active components and targets of 19 high-frequency Chinese medicines obtained from the meta-analysis were screened and explored in network pharmacology analysis. Also, a regulatory network of active components and targets, a core network and key genes, a diagram of protein interaction, network topology analysis, and gene body GO function and KEGG pathway enrichment analysis were performed. A total of 120 active components were identified. NPM1 and HSPA8 are the most critical target proteins in the core network of protein interaction. HSP90AA1 is the most important target protein in the TCM group. KEGG enrichment analysis showed that it was highly significant in the lipid and atherosclerotic pathways. Therefore, moderate evidence revealed that TCM plus chemotherapy has obvious advantages over chemotherapy alone in terms of tumor responses, QOL, peripheral blood lymphocyte levels, and fewer AEs in the treatment of GC. The potential important targets and core genes were displayed.

Analysis on Medication Rules of Chinese Medicinal Herb Formulae in Uterine Subinvolution Treatment Based on Data Mining

Introduction

Uterine subinvolution, especially the subinvolution of the placental site, can be a life-threatening disease that induces secondary postpartum hemorrhage (PPH). Chinese Herbal Medicine has been widely used to improve postpartum recovery and treat uterine subinvolution for thousands of years. Yet, there are many potential laws hidden that are worth exploring.

Methods

Prescriptions treating uterine subinvolution were searched and collected to form datasets. Data mining methods including frequency analysis, cluster analysis, and association rule learning were performed to uncover the potent prescription laws of uterine subinvolution treatment.

Results

A total of 803 formulae involving 249 herbs were obtained. The top 6 most frequently used herbs were Angelicae Sinensis Radix (Danggui), Chuanxiong Rhizoma (Chuanxiong), Leonuri Herba (Yimucao), Persicae Semen (Taoren), Zingiberis Rhizoma Preparatum (Paojiang), and Radix Glycyrrhizae Preparata (Zhigancao). Most of the 249 herbs were being warm in properties, sweet in tastes, and mainly distributed to liver and spleen meridian tropisms. Deficiency-tonifying herbs accounted for the most proportion and heat-clearing herbs ranked the second, followed by blood-activating and stasis-eliminating herbs. 6 clusters were generated by hierarchical clustering, and 5 of them were of clinical significance. 78 rules with support values over 0.25, confidence values over 0.8, and lift values greater than 1 were generated by association rule learning.

Conclusion

The basic principles for uterine subinvolution treatment were deficiency-tonifying, heat-clearing, blood-activating, and stasis-eliminating. Herbs with warm properties, sweet tastes, and liver and spleen meridian tropisms are generally suitable. In addition, Sheng-Hua-Tang was the most frequently used formula for the treatment of uterine subinvolution, yet the dialectical prescriptions were diversified with different patterns/symptoms.

An Improved Deep Learning Model: S-TextBLCNN for Traditional Chinese Medicine Formula Classification

Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination.

Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed.

Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F₁-score of 0.762, both higher than the logistic regression (acc = 0.561, F₁-score = 0.567), SVM (acc = 0.703, F₁-score = 0.591), LSTM (acc = 0.723, F₁-score = 0.621), and TextCNN (acc = 0.745, F₁-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F₁-score is greatly improved by an average of 47.1% in 19 models.

Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.

Different Data Mining Approaches Based Medical Text Data

The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

Efficacy and Safety of Astragalus-Containing Traditional Chinese Medicine Combined With Platinum-Based Chemotherapy in Advanced Gastric Cancer: A Systematic Review and Meta-Analysis

Background

Astragalus-containing traditional Chinese medicine (TCM) is widely used as adjunctive treatment to platinum-based chemotherapy (PBC) in patients with advanced gastric cancer (AGC) in China. However, evidence regarding its efficacy remains limited. This study aimed to evaluate the efficacy and safety of Astragalus-containing TCM combined with PBC in AGC treatment.

Methods

We searched for literature (up to July 19, 2020) in eight electronic databases. The included studies were reviewed by two researchers. The main outcomes were the objective response rate (ORR), disease control rate (DCR), survival rate, quality of life (QOL), adverse drug reactions (ADRs), and peripheral blood lymphocyte levels. The effect estimate of interest was the risk ratio (RR) or mean difference (MD) with 95% confidence intervals (CIs). Trial sequential analysis (TSA) was used to detect the robustness of the primary outcome and to calculate the required information size (RIS). Certainty of the evidence was assessed using the GRADE profiler.

Results

Results based on available literature showed that, compared with patients treated with PBC alone, those treated with Astragalus-containing TCM had a better ORR (RR: 1.24, 95% CI: 1.15–1.34, P < 0.00001), DCR (RR: 1.10, 95% CI: 1.06–1.14, P < 0.00001), 1-year survival rate (RR: 1.41, 95% CI: 1.09–1.82, P = 0.009), 2-year survival rate (RR: 3.13, 95% CI: 1.80–5.46, P < 0.0001), and QOL (RR: 2.03, 95% CI: 1.70–2.43, P < 0.00001 and MD: 12.39, 95% CI: 5.48–19.30, P = 0.0004); higher proportions of CD3⁺ T cells and CD3⁺ CD4⁺ T cells; higher ratio of CD4⁺/CD8⁺ T cells; nature killer cells; and lower incidence of ADRs. Subgroup analysis showed that both oral and injection administration of Astragalus-containing TCM increased tumor response. Whether treatment duration was ≥8 weeks or <8 weeks, Astragalus-containing TCM could increase tumor response in AGC patients. Furthermore, Astragalus-containing TCM combined with oxaliplatin-based chemotherapy could increase the ORR and DCR; when with cisplatin, it could only increase the ORR.

Conclusion

Current low to moderate evidence revealed that Astragalus-containing TCM combined with PBC had better efficacy and less side effects in the treatment of AGC; however, more high-quality randomized studies are warranted.

Systematic Review Registration

PROSPERO, identifier CRD42020203486.

A Data Mining-Based Analysis of Core Herbs on Different Patterns (Zheng) of Non-Small Cell Lung Cancer

Objective

To explore the role of Chinese prescriptions in non-small cell lung cancer (NSCLC) and provide references for the application of herbs and prescriptions.

Methods

Randomized and quasirandomized controlled clinical trials on Chinese herbal medicine in the treatment of NSCLC were collected from seven databases to establish a database of prescriptions on NSCLC. Data-mining analyses were performed by RStudio (v4.0.3) software.

Results

A total of 970 prescriptions were obtained from 945 included studies, involving 7 syndromes and 428 herbs. The main patterns of NSCLC included qi deficiency pattern, yin deficiency pattern, blood deficiency pattern, kidney deficiency pattern, heat toxin pattern, phlegm-dampness pattern, and blood stasis pattern. High-frequency herbs on NSCLC were Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizome (Baizhu), Glycyrrhizae Radix Rhizome (Gancao), Poria (Fuling), Ophiopogonis Radix (Maidong), Hedyotidis Diffusae Herba (Baihuasheshecao), Codonopsis Radix (Dangshen), and Glehniae Radix (Beishashen). The properties of the herbs were mainly cold, warm, and mild. The flavors of the herbs were mainly sweet, bitter, and pungent. The main meridian tropisms were Lung Meridian of Hand-Taiyin, Spleen Meridian of Foot-Taiyin, and Stomach Meridian of Foot-Yangming.

Conclusion

Applying clearing and tonifying method by targeting the lung and spleen was the most frequently used therapy in the treatment of NSCLC. This study offered a glimpse of unique views of traditional Chinese medicine on NSCLC and may benefit the treatment of NSCLC.

The important herbal pair for the treatment of COVID-19 and its possible mechanisms

BACKGROUND

Coronavirus Disease 2019 (COVID-19) is an unprecedented disaster for people around the world. Many studies have shown that traditional Chinese medicine (TCM) are effective in treating COVID-19. However, it is difficult to find the most effective combination herbal pair among numerous herbs, as well as identifying its potential mechanisms. Herbal pair is the main form of a combination of TCM herbs, which is widely used for the treatment of diseases. It can also help us to better understand the compatibility of TCM prescriptions, thus improving the curative effects. The purpose of this article is to explore the compatibility of TCM prescriptions and identify the most important herbal pair for the treatment of COVID-19, and then analyze the active components and potential mechanisms of this herbal pair.

METHODS

We first systematically sorted the TCM prescriptions recommended by the leading experts for treating COVID-19, and the specific herbs contained in these prescriptions across different stages of the disease. Next, the association rule approach was employed to examine the distribution and compatibility among these TCM prescriptions, and then identify the most important herbal pair. On this basis, we further investigated the active ingredients and potential targets in the selected herbal pair by a network pharmacology approach, and analyzed the potential mechanisms against COVID-19. Finally, the main active compounds in the herbal pair were selected for molecular docking with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) 3CLpro and angiotensin converting enzyme II (ACE2) for further verification.

RESULT

We obtained 32 association rules for the herbal combinations in the selection of TCM treatment for COVID-19. The results showed that the combination of Amygdalus Communis Vas (ACV) and Ephedra sinica Stapf (ESS) had the highest confidence degree and lift value, as well as high support degree, which can be used in almost all the stages of COVID-19, so ACV and ESS (AE) were selected as the most important herbal pair. There were 26 active ingredients and 44 potential targets, which might be related to the herbal pair of AE against COVID-19. The main active ingredients of AE against COVID-19 were quercetin, kaempferol, luteolin, while the potential targets were Interleukin 6 (IL-6), Mitogen-activated Protein Kinase 1 (MAPK)1, MAPK8, Interleukin-1β (IL-1β), and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) p65 subunit (RELA). The protein-protein interaction (PPI) cluster demonstrated that IL-6 was the seed in the cluster, which plays an important role in connecting other nodes in the PPI network. The potential pathways mainly involved tumor necrosis factor (TNF), Toll-like receptor (TLR), hypoxia-inducible factor-1 (HIF-1), and nucleotide-binding oligomerization domain (NOD)-like receptor (NLRs). The molecular docking results showed that the main active ingredients of AE have good affinity with SARS-COV-2 3CLpro and ACE2, which are consistent with the above analysis.

CONCLUSIONS

There were 32 association rules in the TCM prescriptions recommended by experts for COVID-19. The combination of ACV and EAS was the most important herbal pair for the treatment of COVID-19. AE might have therapeutic effects against COVID-19 by affecting the inflammatory and immune responses, cell apoptosis, hypoxia damage and other pathological processes through multiple components, targets and pathways.

An Apriori Algorithm-Based Association Rule Analysis to Identify Herb Combinations for Treating Uremic Pruritus Using Chinese Herbal Bath Therapy

Uremic pruritus (UP) is prevalent among patients with end-stage renal disease (ESRD), which causes severe itching and affects their quality of life. Additionally, patients experience fatigue and depression, and an increased risk of mortality has also been reported. A meta-analysis of 17 randomized controlled trials (RCTs) has indicated that Chinese herbal bath therapy (CHBT) had adjuvant benefits in improving UP in ESRD patients, and previous studies have reported that herb combinations were more useful than treatment with a single herb. Association rule analysis has been used to evaluate potential correlations between herb combinations, and Apriori algorithms are one of the most powerful machine-learning algorithms available for identifying associations within databases. Therefore, we used the Apriori algorithm to analyze association rules of potential core herb combinations for use in CHBT for UP treatment using data from a meta-analysis of 17 RCTs that used CHBT for UP treatment. Data on 43 CHBT herbs were extracted from 17 RCTs included for analysis and we found 19 association rules. The results indicated that the following herb combinations {Chuanxiong, Baijili} ≥ {Dahuang} and {Dahuang, Baijili} ≥ {Chuanxiong} were most strongly associated, implying that these herb combinations represent potential CHBT treatments for UP.

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.

Discovery of Herbal Pairs Containing Gastrodia elata Based on Data Mining and the Delphi Expert Questionnaire and Their Potential Effects on Stroke through Network Pharmacology

Background

Traditional Chinese medicine (TCM) formulae can be regarded as a source of new antistroke drugs. The aim of this study was to discover herbal pairs containing Gastrodia elata (Tianma, TM) from formulae based on data mining and the Delphi expert questionnaire. The proposed approach for discovering new herbal combinations, which included data mining, a clinical investigation, and a network pharmacology analysis, was evaluated in this study.

Methods

A database of formulae containing TM was established. All possible herbal pairs were acquired by data mining association rules, and herbal pairs containing TM were screened according to the Support and Confidence levels. Taking stroke as the research object, the relationships between herbal pairs containing TM and stroke were explored by the Delphi expert questionnaire and statistical methods. To explore the effects of herbal pairs containing TM on stroke, a network pharmacology analysis was performed to predict core targets, biological functions, pathways, and mechanisms of action.

Results

A total of 1903 formulae containing TM, involving 896 Chinese herbal medicines (CHMs) and 126 herbal pairs containing RG, were analyzed by association rules. A total of 27 herbal pairs were further screened according to the Support and Confidence levels. Twelve herbal pairs containing RG were added according to the expert questionnaires. Weightiness analysis showed that 9 groups of core herbal pairs contained RG, including TM-QX, TM-JH, TM-CX, TM-GG, TM-SJM, TM-JC, TM-SCP, TM-MJZ, and TM-GT. Two core herbal pairs, TM-JH and TM-CX, were randomly screened to explore their network pharmacological mechanisms in stroke. The important biological targets for network pharmacological analysis of TM-CX and TM-JH related to stroke were PTGS2, ACE, APP, NOS1, and NOS2. An herbal pair-compound-core target-pathway network (H-C-T-P network) was established, and arginine biosynthesis, arginine and proline metabolism, and the relaxin signaling pathway were identified by enrichment analysis.

Conclusion

The herbal pairs of TM-CX and TM-JH obtained from data mining and the expert investigation were found to have effects of preventing and treating stroke through network pharmacology. This could be a viable approach to uncover hidden knowledge about TCM formulae and to discover herbal combinations with clinical and medicinal value based on data mining and questionnaires.

Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification

Endometriosis is a common benign disease in women of reproductive age. It has been defined as a disorder characterized by inflammation, compromised immunity, hormone dependence, and neuroangiogenesis. Unfortunately, the mechanisms of endometriosis have not yet been fully elucidated, and available treatment methods are currently limited. The discovery of new therapeutic drugs and improvements in existing treatment schemes remain the focus of research initiatives. Chinese medicine can improve the symptoms associated with endometriosis. Many Chinese herbal medicines could exert antiendometriosis effects via comprehensive interactions with multiple targets. However, these interactions have not been defined. This study used association rule mining and systems pharmacology to discover a method by which potential antiendometriosis herbs can be investigated. We analyzed various combinations and mechanisms of action of medicinal herbs to establish molecular networks showing interactions with multiple targets. The results showed that endometriosis treatment in Chinese medicine is mainly based on methods of supplementation with blood-activating herbs and strengthening qi. Furthermore, we used network pharmacology to analyze the main herbs that facilitate the decoding of multiscale mechanisms of the herbal compounds. We found that Chinese medicine could affect the development of endometriosis by regulating inflammation, immunity, angiogenesis, and other clusters of processes identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The antiendometriosis effect of Chinese medicine occurs mainly through nervous system–associated pathways, such as the serotonergic synapse, the neurotrophin signaling pathway, and dopaminergic synapse, among others, to reduce pain. Chinese medicine could also regulate VEGF signaling, toll-like reporter signaling, NF-κB signaling, MAPK signaling, PI3K-Akt signaling, and the HIF-1 signaling pathway, among others. Synergies often exist in herb pairs and herbal prescriptions. In conclusion, we identified some important targets, target pairs, and regulatory networks, using bioinformatics and data mining. The combination of data mining and network pharmacology may offer an efficient method for drug discovery and development from herbal medicines.