Publications by authors named "Runshun Zhang"

22 Publications

  • Page 1 of 1

Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions.

Front Pharmacol 2020 20;11:590824. Epub 2021 Jan 20.

Medical Intelligence Institute, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links. We found that the weight distribution of herbal combinations displays a clear power law, which means that most herb pairs were used in low frequency and some herb pairs were used in very high frequency. Furthermore, we found that it displays a clear linear negative correlation between the clustering coefficients and the degree of nodes in the herbal combination network (HCNet). This indicates that hierarchical properties exist in the HCNet. Finally, we investigate the molecular network interaction patterns between herb related target modules (i.e., subnetworks) in herbal prescriptions using a network-based approach and further explore the correlation between the distribution of herb combinations and prescriptions. We found that the more the hierarchical prescription, the better the corresponding effect. The results also reflected a well-recognized principle called "" in TCM formula theories. This also gives references for multidrug combination development in the field of network pharmacology and provides the guideline for the clinical use of combination therapy for chronic diseases.
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http://dx.doi.org/10.3389/fphar.2020.590824DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7854460PMC
January 2021

Topological Analysis of the Language Networks of Ancient Traditional Chinese Medicine Books.

Evid Based Complement Alternat Med 2020 10;2020:8810016. Epub 2020 Dec 10.

Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.

This study aims to explore the topological regularities of the character network of ancient traditional Chinese medicine (TCM) book. We applied the 2-gram model to construct language networks from ancient TCM books. Each text of the book was separated into sentences and a TCM book was generated as a directed network, in which nodes represent Chinese characters and links represent the sequential associations between Chinese characters in the sentences (the occurrence of identical sequential associations is considered as the weight of this link). We first calculated node degrees, average path lengths, and clustering coefficients of the book networks and explored the basic topological correlations between them. Then, we compared the similarity of network nodes to assess the specificity of TCM concepts in the network. In order to explore the relationship between TCM concepts, we screened TCM concepts and clustered them. Finally, we selected the binary groups whose weights are greater than 10 in (ICH, ) and (TCPD, ), hoping to find the core differences of these two ancient TCM books through them. We found that the degree distributions of ancient TCM book networks are consistent with power law distribution. Moreover, the average path lengths of book networks are much smaller than random networks of the same scale; clustering coefficients are higher, which means that ancient book networks have small-world patterns. In addition, the similar TCM concepts are displayed and linked closely, according to the results of cosine similarity comparison and clustering. Furthermore, the core words of and have essential differences, which might indicate the significant differences of language and conceptual patterns between theoretical and clinical books. This study adopts language network approach to investigate the basic conceptual characteristics of ancient TCM book networks, which proposes a useful method to identify the underlying conceptual meanings of particular concepts conceived in TCM theories and clinical operations.
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http://dx.doi.org/10.1155/2020/8810016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748907PMC
December 2020

Disease phenotype synonymous prediction through network representation learning from PubMed database.

Artif Intell Med 2020 01 19;102:101745. Epub 2019 Nov 19.

School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China. Electronic address:

Synonym mapping between phenotype concepts from different terminologies is difficult because terminology databases have been developed largely independently. Existing maps of synonymous phenotype concepts from different terminology databases are highly incomplete, and manually mapping is time consuming and laborious. Therefore, building an automatic method for predictive mapping of synonymous phenotypes is of special importance. We propose a classifier-based phenotype mapping prediction model (CPM) to predict synonymous relationships between phenotype concepts from different terminology databases. The model takes network semantic representations of phenotypes as input and predicts synonymous relationships by training binary classifiers with a voting strategy. We compared the performance of the CPM with a similarity-based phenotype mapping prediction model (SPM), which predicts mapping based on the ranked cosine similarity of candidate mapping concepts. Based on a network representation N2V-TFIDF, with a majority voting strategy method MV, the CPM achieved accuracy of 0.943, which was 15.4% higher than that of the SPM using the cosine similarity method (0.789) and 23.8% higher than that of the SSDTM method (0.724) proposed in our previous work.
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http://dx.doi.org/10.1016/j.artmed.2019.101745DOI Listing
January 2020

Analysis of disease comorbidity patterns in a large-scale China population.

BMC Med Genomics 2019 12 12;12(Suppl 12):177. Epub 2019 Dec 12.

School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.

Background: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.

Methods: We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients.

Results: We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease).

Conclusions: Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.
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http://dx.doi.org/10.1186/s12920-019-0629-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6907122PMC
December 2019

Treatment satisfaction in Chinese medicine outpatient care: a comparison of patients' and doctors' views.

BMC Complement Altern Med 2019 Nov 6;19(1):300. Epub 2019 Nov 6.

China Academy of Chinese Medical Sciences, Beijing, 100700, China.

Background: Both doctors' and patients' opinions are important in the process of treatment and healthcare of Chinese medicine. This study is to compare patients' and doctors' treatment satisfaction over the course of two visits in a Chinese medicine outpatient setting, and to explain their respective views.

Methods: Patients' chief complaints were collected prior to the outpatient encounter. The doctor was then asked (through a questionnaire) to state what complaints he or she was prioritizing during the process of diagnosing disease and making a prescription for herbal medicine or acupuncture treatment. On the next visit, both the patient and the doctor completed a questionnaire assessing satisfaction with the treatment of Chinese medicine prescribed in the first visit and administered by the patient at home. A 5-point Likert scales was used to assess the patients' and doctors' satisfaction with treatment. The timing of the follow-up appointment was determined by the doctor. One chief specialist, one associate chief specialist and one attending practitioner in Chinese medicine, and 60 patients having a follow-up appointment with one of the doctors, participated in the study.

Results: For 11 patients, their most urgent complaint was different from what the doctor's choose to focus on in his or her treatment. And only one patient refused to comply due to his or her dissatisfaction with the treatment focus of the doctor. Overall, 59 patients completed the satisfaction assessment, and 53 patients visited their doctors for a follow-up appointment. Patients' total satisfaction was higher than their doctors' (mean 3.55 vs. 3.45), and correlation of patients' and doctors' treatment satisfaction was moderate (r = 0.63, P < 0.01). Both of the patients' and doctors' satisfaction ratings were correlated with treatment adherence (P < 0.001). The predictors of their treatment satisfaction were different. Doctors' satisfaction with treatment was a significant factor in the process of making further clinical decisions.

Conclusion: Patients and doctors form their opinion about the treatment effects in different ways. When evaluating treatment satisfaction, doctor's opinions are also an important indicator of positive or negative clinical effects and affect the subsequent decisions-making.
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http://dx.doi.org/10.1186/s12906-019-2729-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6836653PMC
November 2019

Symptom network topological features predict the effectiveness of herbal treatment for pediatric cough.

Front Med 2020 Jun 7;14(3):357-367. Epub 2019 Sep 7.

Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.

Pediatric cough is a heterogeneous condition in terms of symptoms and the underlying disease mechanisms. Symptom phenotypes hold complicated interactions between each other to form an intricate network structure. This study aims to investigate whether the network structure of pediatric cough symptoms is associated with the prognosis and outcome of patients. A total of 384 cases were derived from the electronic medical records of a highly experienced traditional Chinese medicine (TCM) physician. The data were divided into two groups according to the therapeutic effect, namely, an invalid group (group A with 40 cases of poor efficacy) and a valid group (group B with 344 cases of good efficacy). Several well-established analysis methods, namely, statistical test, correlation analysis, and complex network analysis, were used to analyze the data. This study reports that symptom networks of patients with pediatric cough are related to the effectiveness of treatment: a dense network of symptoms is associated with great difficulty in treatment. Interventions with the most different symptoms in the symptom network may have improved therapeutic effects.
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http://dx.doi.org/10.1007/s11684-019-0699-3DOI Listing
June 2020

HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction.

IEEE J Biomed Health Inform 2019 07;23(4):1805-1815

The discovery of disease-causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making full use of disease-related (e.g., symptoms) and gene-related (e.g., gene ontology and protein-protein interactions) information to improve the performance of disease gene prediction is still an issue. Here, we develop a heterogeneous disease-gene-related network (HDGN) embedding representation framework for disease gene prediction (called HerGePred). Based on this framework, a low-dimensional vector representation (LVR) of the nodes in the HDGN can be obtained. Then, we propose two specific algorithms, namely, an LVR-based similarity prediction and a random walk with restart on a reconstructed heterogeneous disease-gene network (RW-RDGN), to predict disease genes with high performance. First, to validate the rationality of the framework, we analyze the similarity-based overlap distribution of disease pairs and design an experiment for disease-gene association recovery, the results of which revealed that the LVR of nodes performs well at preserving the local and global network structure of the HDGN. Then, we apply tenfold cross validation and external validation to compare our methods with other well-known disease gene prediction algorithms. The experimental results show that the RW-RDGN performs better than the state-of-the-art algorithm. The prediction results of disease candidate genes are essential for molecular mechanism investigation and experimental validation. The source codes of HerGePred and experimental data are available at https://github.com/yangkuoone/HerGePred.
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http://dx.doi.org/10.1109/JBHI.2018.2870728DOI Listing
July 2019

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Comput Struct Biotechnol J 2019 8;17:282-290. Epub 2019 Feb 8.

School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions.
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http://dx.doi.org/10.1016/j.csbj.2019.02.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396098PMC
February 2019

Heterogeneous network embedding for identifying symptom candidate genes.

J Am Med Inform Assoc 2018 11;25(11):1452-1459

School of Computer and Information Technology and Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China.

Objective: Investigating the molecular mechanisms of symptoms is a vital task in precision medicine to refine disease taxonomy and improve the personalized management of chronic diseases. Although there are abundant experimental studies and computational efforts to obtain the candidate genes of diseases, the identification of symptom genes is rarely addressed. We curated a high-quality benchmark dataset of symptom-gene associations and proposed a heterogeneous network embedding for identifying symptom genes.

Methods: We proposed a heterogeneous network embedding representation algorithm, which constructed a heterogeneous symptom-related network that integrated symptom-related associations and applied an embedding representation algorithm to obtain the low-dimensional vector representation of nodes. By measuring the relevance between symptoms and genes via calculating the similarities of their vectors, the candidate genes of given symptoms can be obtained.

Results: A benchmark dataset of 18 270 symptom-gene associations between 505 symptoms and 4549 genes was curated. We compared our method to baseline algorithms (FSGER and PRINCE). The experimental results indicated our algorithm achieved a significant improvement over the state-of-the-art method, with precision and recall improved by 66.80% (0.844 vs 0.506) and 53.96% (0.311 vs 0.202), respectively, for TOP@3 and association precision improved by 37.71% (0.723 vs 0.525) over the PRINCE.

Conclusions: The experimental validation of the algorithms and the literature validation of typical symptoms indicated our method achieved excellent performance. Hence, we curated a prediction dataset of 17 479 symptom-candidate genes. The benchmark and prediction datasets have the potential to promote investigations of the molecular mechanisms of symptoms and provide candidate genes for validation in experimental settings.
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http://dx.doi.org/10.1093/jamia/ocy117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646926PMC
November 2018

Framing Electronic Medical Records as Polylingual Documents in Query Expansion.

AMIA Annu Symp Proc 2017 16;2017:940-949. Epub 2018 Apr 16.

University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

We present a study of electronic medical record (EMR) retrieval that emulates situations in which a doctor treats a new patient. Given a query consisting of a new patient's symptoms, the retrieval system returns the set of most relevant records of previously treated patients. However, due to semantic, functional, and treatment synonyms in medical terminology, queries are often incomplete and thus require enhancement. In this paper, we present a topic model that frames symptoms and treatments as separate languages. Our experimental results show that this method improves retrieval performance over several baselines with statistical significance. These baselines include methods used in prior studies as well as state-of-the-art embedding techniques. Finally, we show that our proposed topic model discovers all three types of synonyms to improve medical record retrieval.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977599PMC
March 2019

Heterogeneous network propagation for herb target identification.

BMC Med Inform Decis Mak 2018 03 22;18(Suppl 1):17. Epub 2018 Mar 22.

School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.

Background: Identifying targets of herbs is a primary step for investigating pharmacological mechanisms of herbal drugs in Traditional Chinese medicine (TCM). Experimental targets identification of herbs is a difficult and time-consuming work. Computational method for identifying herb targets is an efficient approach. However, how to make full use of heterogeneous network data about herbs and targets to improve the performance of herb targets prediction is still a dilemma.

Methods: In our study, a random walk algorithm on the heterogeneous herb-target network (named heNetRW) has been proposed to identify protein targets of herbs. By building a heterogeneous herb-target network involving herbs, targets and their interactions and simulating random walk algorithm on the network, the candidate targets of the given herb can be predicted.

Results: The experimental results on large-scale dataset showed that heNetRW had higher performance of targets prediction than PRINCE (improved F1-score by 0.08 and Hit@1 by 21.34% in one validation setting, and improved F1-score by 0.54 and Hit@1 by 69.08% in the other validation setting). Furthermore, we evaluated novel candidate targets of two herbs (rhizoma coptidis and turmeric), which showed our approach could generate potential targets that are valuable for further experimental investigations.

Conclusions: Compared with PRINCE algorithm, heNetRW algorithm can fuse more known information (such as, known herb-target associations and pathway-based similarities of protein pairs) to improve prediction performance. Experimental results also indicated heNetRW had higher performance than PRINCE. The prediction results not only can be used to guide the selection of candidate targets of herbs, but also help to reveal the molecule mechanisms of herbal drugs.
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http://dx.doi.org/10.1186/s12911-018-0592-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5872392PMC
March 2018

Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways.

Comput Methods Programs Biomed 2019 Jun 22;174:41-50. Epub 2018 Feb 22.

School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China. Electronic address:

Background And Objective: Liver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment.

Methods: Individualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 6475 liver inpatient cases, we built a liver disease comorbidity network (LDCN) to show the complicated associations between liver diseases and their comorbidities, and then constructed a patient similarity network with shared symptoms (PSN). Finally, we identified liver patient subgroups using community detection methods and performed enrichment analyses to find both distinct clinical and molecular characteristics (with the phenotype-genotype associations and interactome networks) of these patient subgroups.

Results: From the comorbidity network, we found that clinical liver patients have a wide range of disease comorbidities, in which the basic liver diseases (e.g. hepatitis b, decompensated liver cirrhosis), and the common chronic diseases (e.g. hypertension, type 2 diabetes), have high degree of disease comorbidities. In addition, we identified 303 patient modules (representing the liver patient subgroups) from the PSN, in which the top 6 modules with large number of cases include 51.68% of the whole cases and 251 modules contain only 10 or fewer cases, which indicates the manifestation diversity of liver diseases. Finally, we found that the patient subgroups actually have distinct symptom phenotypes, disease comorbidity characteristics and their underlying molecular pathways, which could be used for understanding the novel disease subtypes of liver conditions. For example, three patient subgroups, namely Module 6 (M6, n = 638), M2 (n = 623) and M1 (n = 488) were associated to common chronic liver disease conditions (hepatitis, cirrhosis, hepatocellular carcinoma). Meanwhile, patient subgroups of M30 (n = 36) and M36 (n = 37) were mostly related to acute gastroenteritis and upper respiratory infection, respectively, which reflected the individual comorbidity characteristics of liver subgroups. Furthermore, we identified the distinct genes and pathways of patient subgroups and the basic liver diseases (hepatitis b and cirrhosis), respectively. The high degree of overlapping pathways between them (e.g. M36 with 93.33% shared enriched pathways) indicates the underlying molecular network mechanisms of each patient subgroup.

Conclusions: Our results demonstrate the utility and comprehensiveness of disease classification study based on community detection of patient network using shared TCM symptom phenotypes and it can be used to other more complex diseases.
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http://dx.doi.org/10.1016/j.cmpb.2018.02.014DOI Listing
June 2019

Multistage analysis method for detection of effective herb prescription from clinical data.

Front Med 2018 Apr 14;12(2):206-217. Epub 2017 Jun 14.

Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.

Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.
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http://dx.doi.org/10.1007/s11684-017-0525-8DOI Listing
April 2018

Discussion of solutions to ethical issues in real-world study.

Front Med 2014 Sep 3;8(3):316-20. Epub 2014 Sep 3.

Department of Science and Technology, State Administration of Traditional Chinese Medicine of the People's Republic of China, Beijing, 100027, China.

In recent years, the paradigm of real-world study (RWS) has been at the forefront of clinical research worldwide, particularly in the field of traditional Chinese medicine. In this paper, basic features and nature of real-world clinical studies are discussed, and ethical issues in different stages of RWS are raised and reviewed. Moreover, some preliminary solutions to these issues, such as protecting subjects during the process of RWS and performing ethical review, are presented based on recent practices and basic ethical rules to improve the scientific validity and ethical level of RWS.
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http://dx.doi.org/10.1007/s11684-014-0354-yDOI Listing
September 2014

Experience inheritance from famous specialists based on real-world clinical research paradigm of traditional Chinese medicine.

Front Med 2014 Sep 26;8(3):300-9. Epub 2014 Aug 26.

Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.

The current modes of experience inheritance from famous specialists in traditional Chinese medicine (TCM) include master and disciple, literature review, clinical-epidemiology-based clinical research observation, and analysis and data mining via computer and database technologies. Each mode has its advantages and disadvantages. However, a scientific and instructive experience inheritance mode has not been developed. The advent of the big data era as well as the formation and practice accumulation of the TCM clinical research paradigm in the real world have provided new perspectives, techniques, and methods for inheriting experience from famous TCM specialists. Through continuous exploration and practice, the research group proposes the innovation research mode based on the real-world TCM clinical research paradigm, which involves the inheritance and innovation of the existing modes. This mode is formulated in line with its own development regularity of TCM and is expected to become the main mode of experience inheritance in the clinical field.
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http://dx.doi.org/10.1007/s11684-014-0357-8DOI Listing
September 2014

Clinical data quality problems and countermeasure for real world study.

Front Med 2014 Sep 16;8(3):352-7. Epub 2014 Aug 16.

Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.

Real world study (RWS) has become a hotspot for clinical research. Data quality plays a vital role in research achievement and other clinical research fields. In this paper, the common quality problems in the RWS of traditional Chinese medicine are discussed, and a countermeasure is proposed.
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http://dx.doi.org/10.1007/s11684-014-0351-1DOI Listing
September 2014

Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine.

Front Med 2014 Sep 12;8(3):337-46. Epub 2014 Aug 12.

School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China,

Traditional Chinese medicine (TCM) investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network (CPN), in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.
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http://dx.doi.org/10.1007/s11684-014-0349-8DOI Listing
September 2014

Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes.

Biomed Res Int 2014 2;2014:435853. Epub 2014 Jun 2.

Liaoning Provincial Key Laboratory of Cerebral Diseases, Institute for Brain Disorders, Dalian Medical University, Dalian 116044, China.

Background: Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms.

Methods: This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms.

Results: The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures.

Conclusions: Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.
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http://dx.doi.org/10.1155/2014/435853DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4060751PMC
February 2015

Investigation into the influence of physician for treatment based on syndrome differentiation.

Evid Based Complement Alternat Med 2013 28;2013:587234. Epub 2013 Oct 28.

Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 NanXiaoJie, DongZhiMenNei, Dong Cheng District, Beijing 100700, China.

Background. The characteristics of treatment based on syndrome differentiation (TBSD) cause great challenges to evaluate the effectiveness of the clinical methods. Objectives. This paper aims to evaluate the influence of physician to personalized medicine in the process of TBSD. Methods. We performed a randomized, triple-blind trial involving patients of primary insomnia treated by 3 physicians individually and independently. The patients (n = 30) were randomly assigned to receive treatments by the 3 physicians for every visit. However, they always received the treatment, respectively, prescribed by the physician at the first visit. The primary outcome was evaluated, respectively, by the Pittsburgh Sleep Quality Index (PSQI) and the TCM symptoms measuring scale. The clinical practices of the physicians were recorded at every visit including diagnostic information, syndrome differentiation, treating principles, and prescriptions. Results. All patients in the 3 groups (30 patients) showed significant improvements (>66%) according to the PSQI and TCM symptoms measuring scale. Conclusion. The results indicate that although with comparable effectiveness, there exist significant differences in syndrome differentiation, the treating principles, and the prescriptions of the approaches used by the 3 physicians. This means that the physician should be considered as an important factor for individualized medicine and the related TCM clinical research.
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http://dx.doi.org/10.1155/2013/587234DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3830859PMC
November 2013

Exploring effective core drug patterns in primary insomnia treatment with Chinese herbal medicine: study protocol for a randomized controlled trial.

Trials 2013 Feb 28;14:61. Epub 2013 Feb 28.

Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No,16 NanXiaoJie, DongZhiMenNei, DongCheng District, Beijing, China.

Background: Chinese herbal medicine is one of the most popular Chinese medicine (CM) therapies for primary insomnia. One of the important characteristics of CM is that different Chinese clinicians give different prescriptions even for the same patient. However, there must be some fixed drug patterns in every clinician's prescriptions. This study aims to screen the effective core drug patterns in primary insomnia treatment of three prestigious Chinese clinicians.

Methods/design: A triple-blind, randomized, placebo-controlled, parallel-group clinical trial will be performed. Three clinicians will diagnose and treat every eligible patient individually and independently, producing three prescriptions from three clinicians for every patient. Patients will equally be randomized to one of four groups - medical group A, medical group B, medical group C, or placebo group - and observed for efficacy of treatment. The sample will include primary insomnia patients meeting DSM IV-TR criteria, Spiegel scale score >18, and age 18 to 65 years. A sequential design is employed. Interim analysis will be conducted when between 80 and 160 patients complete the study. The interim study could be stopped and treated as final if a statistically significant difference between treatment and placebo groups can be obtained and core effective drug patterns can be determined. Otherwise, the study continues until the maximum sample size reaches 300. Treatment of the CM group is one of three Chinese clinicians' prescriptions, who provide independently prescriptions based on their own CM theory and the patient's disease condition. Assessment will be by sleep diary and Pittsburgh sleep quality index, and CM symptoms and signs will be measured. Primary outcome is total sleep time. Assessment will be carried out at the washout period, weeks 1, 2, 3, and 4 and 4th week after the end of treatment. Effectiveness analysis will be per intent to treat. A multi-dimension association rule and scale-free networks method will be used to explore the effective core drug patterns.

Discussion: The effective core drug patterns will be found through analyzing several prestigious CM clinicians' treatment information. Screening the effective core drug patterns from prestigious clinicians can accelerate the development of new CM drugs.

Trial Registration: NCT01613183.
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http://dx.doi.org/10.1186/1745-6215-14-61DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3599277PMC
February 2013

Data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches.

Stat Med 2012 Mar 9;31(7):653-60. Epub 2011 Dec 9.

China Academy of Chinese Medical Sciences, Beijing, 100700, China.

Traditional Chinese medicine (TCM) is a clinical-based discipline in which real-world clinical practice plays a significant role for both the development of clinical therapy and theoretical research. The large-scale clinical data generated during the daily clinical operations of TCM provide a highly valuable knowledge source for clinical decision making. Secondary analysis of these data would be a vital task for TCM clinical studies before the randomised controlled trials are conducted. In this article, we discuss the challenges and issues, such as structured data curation, data preprocessing and quality, large-scale data management and complex data analysis requirements, in the data processing and analysis of real-world TCM clinical data. Furthermore, we also discuss related state-of-the-art research and solutions in China. We have shown that the clinical data warehouse based on the collection of structured electronic medical record data and clinical terminology would be a promising approach for generating clinical hypotheses and helping the discovery of clinical knowledge from large-scale real-world TCM clinical data.
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http://dx.doi.org/10.1002/sim.4417DOI Listing
March 2012

A novel approach in discovering significant interactions from TCM patient prescription data.

Int J Data Min Bioinform 2011 ;5(4):353-68

School of Information Technologies, University of Sydney, Sydney, Australia

The efficacy of a traditional Chinese medicine medication derives from the complex interactions of herbs or Chinese Materia Medica in a formula. The aim of this paper is to propose a new approach to systematically generate combinations of interacting herbs that might lead to good outcome. Our approach was tested on a data set of prescriptions for diabetic patients to verify the effectiveness of detected combinations of herbs. This approach is able to detect effective higher orders of herb-herb interactions with statistical validation. We present an exploratory analysis of clinical records using a pattern mining approach called Interaction Rules Mining.
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http://dx.doi.org/10.1504/ijdmb.2011.041553DOI Listing
January 2012