Traditional Chinese Medicine Data Research Articles

Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models.

The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations. This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese Patent Medicine (TCPM) Question Answering Dataset, and the Spleen and Stomach Herbal Prescription Recommendation Dataset. These datasets underpinned the development of the Lingdan Pre-trained LLM and 2 specialized models: the Lingdan-TCPM-Chat Model, which uses a Chain-of-Thought process for symptom analysis and TCPM recommendation, and a Lingdan Prescription Recommendation model (Lingdan-PR) that proposes herbal prescriptions based on electronic medical records. The Lingdan-TCPM-Chat and the Lingdan-PR Model, fine-tuned on the Lingdan Pre-trained LLM, demonstrated state-of-the art performances for the tasks of TCM clinical knowledge answering and herbal prescription recommendation. Notably, Lingdan-PR outperformed all state-of-the-art baseline models, achieving an improvement of 18.39% in the Top@20 F1-score compared with the best baseline. This study marks a pivotal step in merging advanced LLMs with TCM, showcasing the potential of artificial intelligence to help improve clinical decision-making of medical diagnostics and treatment strategies. The success of the Lingdan Pre-trained LLM and its derivative models, Lingdan-TCPM-Chat and Lingdan-PR, not only revolutionizes TCM practices but also opens new avenues for the application of artificial intelligence in other specialized medical fields. Our project is available at https://github.com/TCMAI-BJTU/LingdanLLM.

Analysis of clinical evidence on traditional Chinese medicine for the treatment of diabetic nephropathy: a comprehensive review with evidence mapping.

This study aims to map evidence from Randomized Controlled Trials (RCTs) and systematic reviews/Meta-analyses concerning the treatment of Diabetic Nephropathy (DN) with Traditional Chinese Medicine (TCM), understand the distribution of evidence in this field, and summarize the efficacy and existing problems of TCM in treating DN. The intention is to provide evidence-based data for TCM in preventing and treating DN and to offer a reference for defining future research directions. Comprehensive searches of major databases were performed, spanning from January 2016 to May 2023, to include clinical RCTs and systematic reviews/Meta-analyses of TCM in treating DN. The analysis encompasses the publishing trend of clinical studies, the staging of research subjects, TCM syndrome differentiation, study scale, intervention plans, and outcome indicators. Methodological quality of systematic reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist, and evidence distribution characteristics were analyzed using a combination of text and charts. A total of 1926 RCTs and 110 systematic reviews/Meta-analyses were included. The majority of studies focused on stage III DN, with Qi-Yin deficiency being the predominant syndrome type, and sample sizes most commonly ranging from 60 to 100. The TCM intervention durations were primarily between 12-24 weeks. Therapeutic measures mainly consisted of Chinese herbal decoctions and patented Chinese medicines, with a substantial focus on clinical efficacy rate, TCM symptomatology, and renal function indicators, while attention to quality of life, dosage of Western medicine, and disease progression was inadequate. Systematic reviews mostly scored between 5 and 8 on the AMSTAR scale, and evidence from 94 studies indicated potential positive effects. DN represents a significant health challenge, particularly for the elderly, with TCM showing promise in symptom alleviation and renal protection. Yet, the field is marred by research inconsistencies and methodological shortcomings. Future investigations should prioritize the development of standardized outcome sets tailored to DN, carefully select evaluation indicators that reflect TCM's unique intervention strategies, and aim to improve the robustness of clinical evidence. Emphasizing TCM's foundational theories while incorporating advanced scientific technologies will be essential for innovating research methodologies and uncovering the mechanisms underlying TCM's efficacy in DN management.

Exploration on Standardization of Clinical Research Data in a Clinical Trial Institute of Traditional Chinese Medicine

Traditional Chinese Medicine(TCM) products and practices are widely used worldwide with increasing popularity. The number of TCM clinical trials has increased significantly in the past two decades. There is an urgent need to standardize TCM data and terminology for use in clinical studies. It will ensure data quality, improve the efficiency and expedite scientific communications in TCM. This paper illustrates our exploration processes in the application and development of the data standards in TCM clinical trials. We hope these explorations will help TCM data exchange, sharing, and aggregation which will ultimately accelerate the launch of TCM products and bring benefits to people.

A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining.

Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of great significance to the modernization of TCM. With the development of biomdical text mining technology, TCM has entered the era of intelligence that based on data, and model training increasingly relies on the large-scale labeled data. However, it is difficult to form a large standard data set in the field of TCM due to the low degree of standardization of TCM data collection and the privacy protection of patients' medical records. To solve the above problem, a multi-label deep forest model based on an improved multi-label ReliefF feature selection algorithm, ML-PRDF, is proposed to enhance the representativeness of features within the model, express the original information with fewer features, and achieve optimal classification accuracy, while alleviating the problem of high data processing cost of deep forest models and achieving effective TCM discriminative analysis under small samples. The results show that the proposed model finally outperforms other multi-label classification models in terms of multi-label evaluation criteria, and has higher accuracy in the TCM syndrome differentiation problem compared with the traditional multi-label deep forest, and the comparative study shows that the use of PCC-MLRF algorithm for feature selection can better select representative features.

Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares.

A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods.

The impact of Traditional Chinese Medicine on mouse gut microbiota abundances and interactions based on Granger causality and pathway analysis.

The intestinal microbiota is essential in absorbing nutrients and defending against pathogens and is associated with various diseases, including obesity, type 2 diabetes, and hypertension. As an alternative medicine, Traditional Chinese Medicine (TCM) has long been used in disease treatment and healthcare, partly because it may mediate gut microbiota. However, the specific effects of TCM on the abundance and interactions of microbiota remain unknown. Moreover, using TCM ingredients and data detailing changes in the abundance of gut microorganisms, we developed bioinformatic methods that decipher the impact of TCM on microorganism interactions. The dynamics of gut microorganisms affected by TCM treatments is explored using a mouse model, which provided the abundance of 70 microorganisms over time. The Granger causality analysis was used to measure microorganism interactions. Novel "serial connection" and "diverging connection" models were used to identify molecular mechanisms underlying the impact of TCM on gut microorganism interactions, based on microorganism proteins, TCM chemical ingredients, and KEGG reaction equations. Codonopsis pilosula (Dangshen), Cassia twig (Gui Zhi), Radices saussureae (Mu Xiang), and Sijunzi Decoction did not cause an increase in the abundance of harmful microorganisms. Most TCMs decreased the abundance of Bifidobacterium pseudolongum, suggesting a Bifidobacterium pseudolongum supplement should be used during TCM treatment. The Granger causality analysis indicated that TCM treatment changes more than half the interactions between the 70 microorganisms, and "serial connection" and "diverging connection" models suggested that changes in interactions may be related to the reaction number connecting species proteins and TCM ingredients. From a species diversity perspective, a TCM decoction is better than a single herb for healthcare. The Sijunzi Decoction only significantly increased the abundance of Bifidobacterium pseudolongum and did not cause a decrease in the abundance of other species but was found to improve the alpha diversity with the lowest replacement rate. Because most of the nine TCMs are medicinal and edible plants, we expect the methods and results presented can be used to optimize and integrate microbiota and TCMs into healthcare processes. Moreover, as a control study, these results can be combined with future disease mouse models to link variations in species abundance with particular diseases.

A KG-Enhanced Multi-Graph Neural Network for Attentive Herb Recommendation.

Traditional Chinese Medicine (TCM) has the longest clinical history in Asia and contributes a lot to health maintenance worldwide. An essential step during the TCM diagnostic process is syndrome induction, which comprehensively analyzes the symptoms and generates an overall summary of the symptoms. Given a set of symptoms, the existing herb recommenders aim to generate the corresponding herbs as a treatment by inducing the implicit syndrome representations based on TCM prescriptions. As different symptoms have various importance during the comprehensive consideration, we argue that treating the co-occurred symptoms equally to do syndrome induction in the previous studies will lead to the coarse-grained syndrome representation. In this paper, we bring the attention mechanism to model the syndrome induction process. Given a set of symptoms, we leverage an attention network to discriminate the symptom importance and adaptively fuse the symptom embeddings. Besides, we introduce a TCM knowledge graph to enrich the input corpus and improve the quality of representation learning. Further, we build a KG-enhanced Multi-Graph Neural Network architecture, which performs the attentive propagation to combine node feature and graph structural information. Extensive experimental results on two TCM data sets show that our proposed model has the outstanding performance over the state-of-the-arts.

Analysis of Medication Rule of Primary Epilepsy Based on Xiaocheng Yan's Clinical Experience Collection of Epilepsy.

Objective To explore and analyze the medication rule of Professor Xiaocheng Yan in the treatment of primary epilepsy, hoping to provide reference for the clinical treatment of primary epilepsy. Methods Mining and analysis of Professor Xiaocheng Yan sorted out the medical cases of primary epilepsy in Xiaocheng Yan's clinical experience collection of epilepsy, extracted the traditional Chinese medicine (TCM) prescription data in the medical cases, standardized the obtained TCM prescription data, and used the data mining function integrated by the ancient and modern medical case cloud platform V2.3.5 to carry out frequency statistics, cluster analysis, association analysis, and complex network analysis on the TCM data, and the common herbs used by Professor Xiaocheng Yan in the treatment of primary epilepsy, properties and classifications of commonly used herbs, pairs of commonly used herbs, and core prescriptions were obtained. Results A total of 39 cases, 228 medical records, and 230 prescriptions data of TCM were included. A total of 96 Chinese medicinal herbs were involved, and the total frequency of medication was 3,828. High-frequency herbs include Rhizoma Gastrodiae (Tianma) (222 times), Ramulus Uncariae cum Uncis (Gouteng) (220 times), Rhizoma Acori Tatarinowii (Shichangpu) (216 times), Rhizoma Pinelliae Praeparatum (Fabanxia) (207 times), Bombyx Batryticatus (Jiangcan) (206 times), and Periostracum Cicadae (Chantui) (181 times). The main properties and flavors of commonly used Chinese medicinal herbs were sweet, bitter, and pungent, which were mainly attributed to the four meridians of liver, lung, heart, and spleen. Commonly used couplet herbs were {Periostracum Cicadae (Chantui)} ≥ {Bombyx Batryticatus (Jiangcan)}, {Rhizoma Acori Tatarinowii (Shichangpu)} ≥{ Bombyx Batryticatus (Jiangcan)}, {Radix Bupleuri (Chaihu)} ≥ {Radix Scutellariae (Huangqin)}, {Rhizoma Gastrodiae (Tianma)} ≥ {Ramulus Uncariae cum Uncis (Gouteng)}, {Rhizoma Acori Tatarinowii (Shichangpu)} ≥ {Periostracum Cicadae (Chantui)}, {Ramulus Uncariae cum Uncis (Gouteng)} ≥ {Bombyx Batryticatus (Jiangcan)}, {Bombyx Batryticatus (Jiangcan)} ≥ {Rhizoma Gastrodiae (Tianma)}, {Rhizoma Acori Tatarinowii (Shichangpu)} ≥ {Ramulus Uncariae cum Uncis (Gouteng)}, etc. The core prescription composition was based on the addition and subtraction of Tianma Gouteng decoction and Erchen decoction. The main pharmacological mechanisms of core prescriptions are mainly reflected in antioxidation, enhancing GABA efficacy, and regulating NMDA channel and sodium channel, neuroprotection, and so on. Conclusion Professor Xiaocheng Yan's medication for the treatment of primary epilepsy was based on the principle of relieving wind and spasm, drying dampness and resolving phlegm, giving consideration to both Qi and blood, and harmonizing liver, lung, heart, and spleen.

Is Traditional Chinese Medicine Effective in Treating Tic Disorders (Tourette Syndrome)? A Meta-Analysis of Randomized Controlled Trials

Introduction: Traditional Chinese medicine (TCM) is a type of alternative medicine that has been widely utilized in the prevention and treatment of neuropsychiatric diseases such as schizophrenia, ADHD, Alzheimer disease, and Tourette syndrome (TS) in many countries. However, the results regarding TCM’s effectiveness and safety in treating TS are conflicting. Therefore, the goal of the present study is to analyze and summarize the available literature related to the efficacy of TCM in the treatment of tic disorders, especially TS. Methods: Relevant articles for the meta-analysis were searched using appropriate keywords from PubMed, MEDLINE, and CENTRAL databases. Event data for TCM for the treatment of TS in the control and intervention arms were determined from the randomized controlled trials, and diagnostic odds ratio (DOR) and risk of bias analysis were performed. Meta-analysis was performed using MedCalc software, and suitable parameters to assess heterogeneity were computed. Results: Twelve randomized clinical trials with a total of 1,681 TS patients were included as per the inclusion criteria. The patients’ clinical symptoms were improved to a greater extent, and the pooled DOR of 1.311 with a 95% confidence interval (CI) value ranging from 0.804 to 2.139 and I² value of 72.04% were obtained. The pooled relative risk was 1.09, with a 95% CI value ranging from 0.97 to 1.268. The data heterogeneity was assessed, and we achieved a Q value of 33.54, a p value of 0.0004, and I² value of 67.21% with a 95% CI value of 32.91–82.10. All these values are statistically significant, with p < 0.005, and confirmed the effectiveness of TCM for TS patients. Conclusion: The present meta-analysis on account of these statistically significant results highly recommends using TCM to treat TS.

HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine.

Pharmacotranscriptomics has become a powerful approach for evaluating the therapeutic efficacy of drugs and discovering new drug targets. Recently, studies of traditional Chinese medicine (TCM) have increasingly turned to high-throughput transcriptomic screens for molecular effects of herbs/ingredients. And numerous studies have examined gene targets for herbs/ingredients, and link herbs/ingredients to various modern diseases. However, there is currently no systematic database organizing these data for TCM. Therefore, we built HERB, a high-throughput experiment- and reference-guided database of TCM, with its Chinese name as BenCaoZuJian. We re-analyzed 6164 gene expression profiles from 1037 high-throughput experiments evaluating TCM herbs/ingredients, and generated connections between TCM herbs/ingredients and 2837 modern drugs by mapping the comprehensive pharmacotranscriptomics dataset in HERB to CMap, the largest such dataset for modern drugs. Moreover, we manually curated 1241 gene targets and 494 modern diseases for 473 herbs/ingredients from 1966 references published recently, and cross-referenced this novel information to databases containing such data for drugs. Together with database mining and statistical inference, we linked 12 933 targets and 28 212 diseases to 7263 herbs and 49 258 ingredients and provided six pairwise relationships among them in HERB. In summary, HERB will intensively support the modernization of TCM and guide rational modern drug discovery efforts. And it is accessible through http://herb.ac.cn/.

Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example

‘Polypharmacology’ is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the ‘multi-components, multi-targets and multi-pathways’ principle. It is a challenge to investigate the polypharmacology mechanism of TCM with multiple components. In this study, we used XiaoErFuPi (XEFP) granules as an example to describe an unsupervised learning strategy for polypharmacology research of TCM and to explore the mechanism of XEFP polypharmacology against multifactorial disease function dyspepsia (FD). Unsupervised clustering of compounds based on similarity evaluation of cellular function fingerprints showed that compounds of TCM without similar targets and chemical structure could also exert similar therapeutic effects on the same disease, as different targets participate in the same pathway closely associated with the pathological process. In this study, we proposed an unsupervised machine learning strategy for exploring the polypharmacology-based mechanism of TCM, utilizing hierarchical clustering based on cellular functional similarity, to establish a connection from the chemical clustering module to cellular function. Meanwhile, FDA-approved drugs against FD were used as references for the mechanism of action (MoA) of FD. First, according to the compound-compound network built by the similarity of cellular function of XEFP compounds and FDA-approved FD drugs, the possible therapeutic function of TCM may represent a known mechanism of FDA-approved drugs. Then, as unsupervised learning, hierarchical clustering of TCM compounds based on cellular function fingerprint similarity could help to classify the compounds into several modules with similar therapeutic functions to investigate the polypharmacology effect of TCM. Furthermore, the integration of quantitative omics data of TCM and approved drugs (from LINCS datasets) provides more quantitative evidence for TCM therapeutic function consistency with approved drugs. A spasmolytic activity experiment was launched to confirm vanillic acid activity to repress smooth muscle contraction; vanillic acid was also predicted to be active compound of XEFP, supporting the accuracy of our strategy. In summary, the approach proposed in this study provides a new unsupervised learning strategy for polypharmacological research investigating TCM by establishing a connection between the compound functional module and drug-activated cellular processes shared with FDA-approved drugs, which may elucidate the unique mechanism of traditional medicine using FDA-approved drugs as references, facilitate the discovery of potential active compounds of TCM and provide new insights into complex diseases.

Clinical research for whether the Traditional Chinese medicine could promote the resorption of lumbar disc herniation: a randomized controlled trial.

Lumbar disc herniation (LDH) is a common, disabling musculoskeletal disorder. Magnetic resonance imaging has clarified the natural history of lumbar disc lesions and has documented that disc lesions can become smaller and can even be completely resorbed. Previous studies have confirmed that some traditional Chinese medicine (TCM) therapies can promote resorption of the protrusion. However, high-quality research evidence is needed to support the effectiveness of the protocol.Objective:This clinical trial aims to establish whether TCM can promote the resorption of LDH and to assess the efficacy of such therapy for LDH, thereby evaluating its clinical effect.Methods:The present study design is for a single-center, 2-arm, open-label randomized controlled trial. A total of 150 eligible LDH patients will be randomly assigned to either a TCM treatment group or a control group in a 1:1 ratio. Patients in the TCM group will be administered a TCM decoction for 4 weeks. Patients in the conventional drug control group will be instructed to take a specific daily dose of celecoxib. The primary outcome measure is the change from baseline in the volume of the protrusion, as assessed using MR images. Secondary outcome measures include visual analog scale pain scores and Japanese Orthopaedic Association scores assessed at 3 and 6 months.Discussion:The design and methodological rigor of this trial will allow evaluation of the basic clinical efficacy and safety data for TCM in the treatment of patients with LDH. The trial will also assess whether TCM can promote the resorption of LDH. This research will therefore help provide a solid foundation for the clinical treatment of LDH and for future research in TCM therapy.Trial registration:ChiCTR1900022377.

A Traditional Chinese Medicine Prescription Recommendation method based on Mutual Information Clustering

Traditional Chinese medicine (TCM) data is the main knowledge resource of TCM, which contains a wealth of clinical experience knowledge. Machine learning has made remarkable achievements in natural language processing. As the carrier of TCM knowledge and information stored in the form of text, using machine learning method to study these TCM data can save a lot of manpower cost, improve the objectivity of TCM, promote TCM related knowledge better, and have certain guiding significance for the research of TCM human engineering experiment. This paper proposes a recommendation algorithm based on mutual information clustering. Its core idea is calculating mutual information between two symptoms, and set symptom “relatives and friends group”, after getting the symptom clustering results of mutual information, then combine the clustering results and search algorithm to achieve the effect of recommendation and filtering. Experimental results show that the proposed method is effective.

Discovery of potential asthma targets based on the clinical efficacy of Traditional Chinese Medicine formulas

Ethnopharmacological relevanceStandard therapy for asthma, a highly heterogeneous disease, is primarily based on bronchodilators and immunosuppressive drugs, which confer short-term symptomatic relief but not a cure. It is difficult to discover novel bronchodilators, although potential new targets are emerging. Traditional Chinese Medicine (TCM) formulas have been used to treat asthma for more than 2000 years, forming the basis for representative asthma treatments. Aim of the studyBased on the efficacy of TCM formulas, anti-asthmatic herbal compounds bind proteins are potential targets for asthma therapy. This analysis will provide new drug targets and discovery strategies for asthma therapy. Materials and methodsA list of candidate herbs for asthma was selected from the classical formulas (CFs) of TCM for the treatment of wheezing or dyspnea recorded in Treatise on Cold Damage and Miscellaneous Diseases (TCDMD) and from modern herbal formulas identified in the SAPHRON TCM Database using the keywords “wheezing” or “dyspnea”. Compounds in the selected herbs and compounds that directly bind target proteins were acquired by searching the Herbal Ingredients' Targets Database (HITD), TCM Data Bank (TCMDB) and TCM Integrated Database (TCMID). Therapeutic targets of conventional medicine (CM) for asthma were collected by searching Therapeutic Target Database (TTD), DrugBank and PubMed as supplements. Finally, the enriched gene ontology (GO) terms of the targets were obtained using the Database for Annotation Visualization and Integrated Discovery (DAVID) and protein-protein interactions (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). The effects of two selected TCM compounds, kaempferol and ginkgolide A, on cellular resistance in human airway smooth muscle cells (ASMCs) and pulmonary resistance in a mouse model were investigated. ResultsThe list of 32 candidate herbs for asthma was selected from 10 CFs for the treatment of wheezing or dyspnea recorded in TCDMD and 1037 modern herbal formulas obtained from the SAPHRON TCM Database. A total of 130 compounds from the 32 selected herbs and 68 herbal compounds directly bind target proteins were acquired from HITD and TCMDB. Eighty-eight therapeutic targets of CM for asthma were collected by searching TTD and PubMed as supplements. DAVID and STRING analyses showed targets of TCM formulas are primarily related to cytochrome P450 (CYP) family, transient receptor potential (TRP) channels, matrix metalloproteinases (MMPs) and ribosomal protein. Both TCM formulas and CM act on the same types of targets or signaling pathways, such as G protein-coupled receptors (GPCRs), steroid hormone receptors (SHRs), and JAK-STAT signaling pathway. The proteins directly targeted by herbal compounds, TRPM8, TRPA1, TRPV3, CYP1B1, CYP2B6, CYP1A2, CYP3A4, CYP1A1, PPARA, PPARD, NR1I2, MMP1, MMP2, ESR1, ESR2, RPLP0, RPLP1 and RPLP2, are potential targets for asthma therapy. In vitro results showed kaempferol (1 × 10−2 mM) and ginkgolide A (1 × 10−5 mM) significantly increased the cell index (P < 0.05 vs. histamine, n = 3) and therefore relaxed human ASMCs. In vivo results showed kaempferol (145 μg/kg) and ginkgolide A (205 μg/kg) significantly reduced pulmonary resistance (P < 0.05 vs. methacholine, n = 6). ConclusionPotential target discovery for asthma treatment based on the clinical effectiveness of TCM is a feasible strategy.

Quantitative knowledge presentation models of traditional Chinese medicine (TCM): A review.

Modern computer technology sheds light on new ways of innovating Traditional Chinese Medicine (TCM). One method that gets increasing attention is the quantitative research method, which makes use of data mining and artificial intelligence technology as well as the mathematical principles in the research on rationales, academic viewpoints of famous doctors of TCM, dialectical treatment by TCM, clinical technology of TCM, the patterns of TCM prescriptions, clinical curative effects of TCM and other aspects. This paper reviews the methods, means, progress and achievements of quantitative research on TCM. In the core database of the Web of Science, "Traditional Chinese Medicine", "Computational Science" and "Mathematical Computational Biology" are selected as the main retrieval fields, and the retrieval time interval from 1999 to 2019 is used to collect relevant literature. It is found that researchers from China Academy of Chinese Medical Sciences, Zhejiang University, Chinese Academy of Sciences and other institutes have opened up new methods of research on TCM since 2009, with quantitative methods and knowledge presentation models. The adopted tools mainly consist of text mining, knowledge discovery, technologies of the TCM database, data mining and drug discovery through TCM calculation, etc. In the future, research on quantitative models of TCM will focus on solving the heterogeneity and incompleteness of big data of TCM, establishing standardized treatment systems, and promoting the development of modernization and internationalization of TCM.

Research on Herb Pairs of Classical Formulae of ZHANG Zhong-Jing Using Big Data Technology

ObjectiveTo analyze various herbal combinations in Treatise on Exogenous Febrile Diseases (Shang Han Lun,《伤寒论》) and Synopsis of Prescriptions of the Golden Chamber (Jin Gui Yao Lve, 《金匮要略》), seeking to identify fundamental rules dictating the selection of herbal combinations through probability models and big data technology. MethodsA total of 252 formulae were collected from Treatise on Exogenous Febrile Diseases (Shang Han Lun, 《伤寒论》) and Synopsis of Prescriptions of the Golden Chamber (Jin Gui Yao Lve, 《金匮要略》) by ZHANG Zhong-Jing. Formulae were then preprocessed with all herb names standardized. The concepts of candidate herb pair and candidate herb pair probability were proposed to analyze the rules of combinations in classical formulae based on probability statistics. MapReduce parallel computing framework of distributed big data technology was adopted to analyze large data samples combined with inverted index algorithm. ResultsThe results showed that the core herbs were Glycyrrhizae Radix Rhizoma (Gan Cao, 甘 草 ), Cinnamomi Ramulus (Gui Zhi, 桂 枝 ), Zingiberis Rhizoma Recens (Sheng Jiang, 生姜), Jujubae Fructus (Da Zao, 大枣), Paeoniae Radix Alba (Bai Shao, 白 芍 ), etc. 43 high-frequency pairs co-occurring 10 times or above were extracted, and 35 of these combinations were recognized as traditional herb pairs, such as Cinnamomi Ramulus (Gui Zhi, 桂枝) - Glycyrrhizae Radix Rhizoma (Gan Cao, 甘草), Zingiberis Rhizoma Recens (Sheng Jiang, 生姜) - Jujubae Fructus (Da Zao, 大枣), and Cinnamomi Ramulus (Gui Zhi, 桂枝) - Ginseng Radix Et Rhizoma (Ren Shen, 人参). The other 8 pairs of combinations, such as Paeoniae Radix Alba (Bai Shao, 白芍) - Zingiberis Rhizoma Recens (Sheng Jiang, 生姜), Paeoniae Radix Alba (Bai Shao, 白 芍 ) - Jujubae Fructus (Da Zao, 大 枣 ), and Zingiberis Rhizoma Recens (Sheng Jiang, 生姜) - Ginseng Radix Et Rhizoma (Ren Shen, 人参), were not defined traditionally, but in connection with commonly used herbs. Classical formulae took the core herbs as principles, focusing on tonifying deficiency, strengthening the spleen and the stomach, strengthening the healthy Qi, and eliminating pathogenic factors. The compatibility pattern of properties involved was mainly acrid and sweet, which reflected the compatibility laws of benefiting Qi and tonifying Yang, replenishing Qi and nourishing blood, etc. ConclusionsThe research of classical formulae provides common understanding of some basic rules that have been adopted to tackle common illnesses/diseases using herbal medicine. The results help to reinforce theoretical understanding and development of traditional Chinese medicine (TCM), and revealing the hidden rules of combination in TCM data. Analyzing wider data samples of various herbal combinations through computation and big data technology can further optimize the use of TCM.

A novel Chinese herbal medicine clustering algorithm via artificial bee colony optimization.

Traditional Chinese medicine (TCM) has become popular and been viewed as an effective clinical treatment across the world. Accordingly, there is an ever-increasing interest in performing data analysis over TCM data. Aiming to cope with the problem of excessively depending on empirical values when selecting cluster centers by traditional clustering algorithms, an improved artificial bee colony algorithm is proposed by which to automatically select cluster centers and apply it to aggregate Chinese herbal medicines. The proposed method integrates the following new techniques: (1) improving the artificial bee colony algorithm by applying a new searching strategy of neighbour nectar, (2) employing the improved artificial bee colony algorithm to optimize the parameters of the cutoff distance dc, the local density ρi and the minimum distance δi between the element i and any other element with higher density in the cluster algorithm by fast search and finding of density peaks (called DP algorithm) to find the optimal cluster centers, in order to clustering herbal medicines in an accurate fashion with the guarantee of runtime performance. Extensive experiments were conducted on the UCI benchmark datasets and the TCM datasets and the results verify the effectiveness of the proposed method by comparing it with classical clustering algorithms including K-means, K-mediods and DBSCAN in multiple evaluation metrics, that is, Silhouette Coefficient, Entropy, Purity, Precision, Recall and F1-Measure. The results show that the IABC-DP algorithm outperforms other approaches with good clustering quality and accuracy on the UCI and the TCM datasets as well. In addition, it can be found that the improved artificial bee colony algorithm can effectively reduce the number of iterations when compared to the traditional bee colony algorithm. In particular, the IABC-DP algorithm is applied to cluster multi-dimensional Chinese herbal medicines and the result shows that it outperforms other clustering algorithms in clustering Chinese herbal medicines, which can contribute to a larger effort targeted at advancing the study of discovering composition rules of traditional Chinese prescriptions.

A Multi-Center Cross-Sectional Study of the Status of Patient-Oriented Chinese Medicine Treatment of Malignant Tumors

Objective: To clarify the current status of traditional Chinese medicine (TCM) application and research in the field of tumor treatment, and provide reference guidance for the rational application of TCM in the treatment of tumors, multidisciplinary cooperation in tumor treatment, and improvement of TCM education. Methods: From July 2015 to June 2016, the patients in oncology department of four large-scale comprehensive first-class hospitals in China were selected. The general clinical data, clinical application of TCM and cognitive status data of tumor patients were obtained by using electronic medical record database combined with questionnaire to explore the clinical application of TCM in tumor treatment from the perspective of tumor patients. Results: The proportion of patients receiving chemotherapy (including targeted therapy) and TCM treatment was 28.7%. 16.6% of doctors were concerned that TCM combined with chemotherapy might produce adverse reactions. 20.0% of doctors think they have enough knowledge to answer questions related to TCM. 22.0% of doctors have received continuing education in TCM. During the treatment, 36.5% of patients had discussed TCM with their doctors. A total of 44.9% of doctors believe that TCM treatment is effective. Conclusion: The purpose of applying TCM is mainly to regulate the body’s immunity, improve the symptoms such as loss of appetite, fatigue and sleep disorder, increase the effect and reduce toxicity of radiotherapy and chemotherapy. The treatment is mainly based on TCM compounds and proprietary Chinese medicines.

Traditional Chinese Medicine data management policy in big data environment

As traditional data management model cannot effectively manage the massive data in traditional Chinese medicine(TCM) due to the uncertainty of data object attributes as well as the diversity and abstraction of data representation, a management strategy for TCM data based on big data technology is proposed. Based on true characteristics of TCM data, this strategy could solve the problems of the uncertainty of data object attributes in TCM information and the non-uniformity of the data representation by using modeless properties of stored objects in big data technology. Hybrid indexing mode was also used to solve the conflicts brought by different storage modes in indexing process, with powerful capabilities in query processing of massive data through efficient parallel MapReduce process. The theoretical analysis provided the management framework and its key technology, while its performance was tested on Hadoop by using several common traditional Chinese medicines and prescriptions from practical TCM data source. Result showed that this strategy can effectively solve the storage problem of TCM information, with good performance in query efficiency, completeness and robustness.

The gene expression profiles in response to 102 traditional Chinese medicine (TCM) components: a general template for research on TCMs

Traditional Chinese medicines (TCMs) have important therapeutic value in long-term clinical practice. However, because TCMs contain diverse ingredients and have complex effects on the human body, the molecular mechanisms of TCMs are poorly understood. In this work, we determined the gene expression profiles of cells in response to TCM components to investigate TCM activities at the molecular and cellular levels. MCF7 cells were separately treated with 102 different molecules from TCMs, and their gene expression profiles were compared with the Connectivity Map (CMAP). To demonstrate the reliability and utility of our approach, we used nitidine chloride (NC) from the root of Zanthoxylum nitidum, a topoisomerase I/II inhibitor and α-adrenoreceptor antagonist, as an example to study the molecular function of TCMs using CMAP data as references. We successfully applied this approach to the four ingredients in Danshen and analyzed the synergistic mechanism of TCM components. The results demonstrate that our newly generated TCM data and related methods are valuable in the analysis and discovery of the molecular actions of TCM components. This is the first work to establish gene expression profiles for the study of TCM components and serves as a template for general TCM research.