DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning.

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.

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

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.

Heterogeneous network propagation for herb target identification.

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.

[Effect of jianpi tongluo jiedu recipe on expression levels of COX-2, NF-kappaBp65, and Bcl-2 in gastric mucosa of patients with precancerous lesions of gastric cancer].

OBJECTIVE: To explore the effect of Jianpi Tongluo Jiedu Recipe (JTJR) on protein expression levels of COX-2, NF-kappaBp65, Bcl-2, and Bax, mRNA expression levels of COX-2 and Bcl-2, and the apoptotic index (Al) in gastric mucosa of patients with precancerous lesions of gastric cancer (PL-GC). METHODS: Totally 65 PLGC patients were recruited and treated by JTJR (modified by syndrome typing), one dose per day for six successive months. Protein expression levels of COX-2, NF-KBp65, Bcl-2, and Bax were detected in 65 patients using immunohistochemical (IHC) assay before and after treatment. mRNA expression levels of COX-2 and Bcl-2 were detected in 54 patients using reverse transcription-polymerase chain reaction (RT-PCR). Meanwhile, changes of Al was detected in 65 patients using TdT-mediated dUTP-biotin nick end labeling (TUNEL) fluorescence method. RESULTS: After treatment with JTJR, positive protein expression levels of COX-2, NF-KBp65, and Bcl-2 were obviously decreased in the gastric mucosa of PLGC patients (P <0.01), but Bax positive protein expression was found to be higher (P < 0.05). At the same time mRNA expression levels of COX-2 and Bcl-2 were significantly lower after treatment than before treatment (P < 0.05, P < 0.01); Al also increased after treatment (P < 0.05). CONCLUSION: JTJR could promote apoptosis possibly via NF-kappaBp65/COX-2, COX-2/Bcl-2, and NF-kappaBp65/Bcl-2 signaling pathways, thereby affecting PLGC patients.

Efficacy of Chinese Medicine Treatment Based on Syndrome Differentiation for Primary Insomnia: A Randomized Placebo Controlled Triple-Blinded Trial.

OBJECTIVE: To assess efficacy of Chinese medicine (CM) on insomnia considering characteristics of treatment based on syndrome differentiation. METHODS: A total of 116 participants aged 18 to 65 years with moderate and severe primary insomnia were randomized to the placebo (n=20) or the CM group (n=96) for a 4-week treatment and a 4-week follow-up. Three CM clinicians independently prescribed treatments for each patient based on syndromes differentiation. The primary outcome was change in total sleep time (TST) from baseline. Secondary endpoints included sleep onset latency (SOL), wake time after sleep onset (WASO), sleep efficiency, Pittsburgh Sleep Quality Index (PSQI) and CM symptoms. RESULTS: The CM group had an average 0.6 h more (95% confidence interval (CI): 0.3-0.9, P<0.001) TST and 34.1% (10.3%-58.0%, P=0.005) more patients beyond 0.5 h TST increment than that of the placebo group. PSQI was changed -3.3 (-3.8 to -2.7) in the CM group, a -2.0 (-3.2 to -0.8, P<0.001) difference from the placebo group. The CM symptom score in the CM group decreased -2.0 (-3.3 to -0.7, P=0.003) more than the placebo group. SOL and WASO changes were not significantly different between groups. The analysis of prescriptions by these clinicians revealed blood deficiency and Liver stagnation as the most common syndromes. Prescriptions for these clinicians displayed relative stability, while the herbs varied. All adverse events were mild and were not related to study treatment. CONCLUSION: CM treatment based on syndrome differentiation can increase TST and improve sleep quality of primary insomnia. It is effective and safe for primary insomnia. In future studies, the long-term efficacy validation and the exploratory of eutherapeutic clinicians' fixed herb formulas should be addressed (Registration No. NCT01613183).

[Discussion on solutions to ethical issues of clinical researches in a real world].

The paradigm of a real world study has become the frontiers of clinical researches, especially in the field of Chinese medicine, all over the world in recent years. In this paper, ethical issues which probably exist in real-world studies are raised and reviewed. Moreover, some preliminary solutions to these issues such as protecting subjects during the process of real-world studies and performing ethical review are raised based on recent years' practices to enhance the scientificity and ethical level of real-world studies.

[Method for direct generation data for formatted case report forms based on requirement for data authenticity in actual clinical conditions].

Data authenticity is the basic requirement of clinical studies. In actual clinical conditions how to establish formatted case report forms (CRF) in line with the requirement for data authenticity is the key to ensure clinical data quality. On the basis of the characteristics of clinical data in actual clinical conditions, we determined elements for establishing formatted case report forms by comparing differences in data characteristics of CRFs in traditional clinical studies and in actual clinical conditions, and then generated formatted case report forms in line with the requirement for data authenticity in actual clinical conditions. The data of formatted CRFs generated in this study could not only meet the requirement for data authenticity of clinical studies in actual clinical conditions, but also comply with data management practices for clinical studies, thus it is deemed as a progress in technical methods.

Head-to-head comparison of 68Ga-FAPI-04 PET/CT and 18F-FDG PET/CT in the evaluation of primary digestive system cancer: a systematic review and meta-analysis.

Introduction: Althoug 18F-FDG positron emission tomography/computed tomography (PET/CT) is widely accepted as a diagnostic tool for detecting digestive cancers, 68Ga-FAPI-04 PET/CT may perform better in detecting gastrointestinal malignancies at an earlier stage. This study aimed to systematically review the diagnostic performance of 68Ga-FAPI-04 PET/CT compared with that of 18F-FDG PET/CT in primary digestive system cancers. Methods: In this study, a comprehensive search using the PubMed, EMBASE, and Web of Science databases was performed to identify studies that met the eligibility criteria from the beginning of the databases to March 2023. The quality of the relevant studies with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) method was assessed using the RevMan 5.3 software. Sensitivity and specificity were calculated using bivariate random-effects models, and heterogeneity was assessed with the I2 statistic and meta-regression analysis using the R 4.22 software. Results: A total of 800 publications were identified in the initial search. Finally, 15 studies comprising 383 patients were included in the analysis. The pooled sensitivity and specificity of 68Ga-FAPI-04 PET/CT were 0.98 (95% CI, 0.94-1.00) and 0.81 (95% CI, 0.23-1.00), whereas those of 18F-FDG PET/CT were 0.73 (95% CI, 0.60-0.84) and 0.77 (95% CI, 0.52-0.95), respectively. 68Ga-FAPI-04 PET/CT performed better for specific tumours, particularly in gastric, liver, biliary tract, and pancreatic cancers. Both imaging modalities had essentially the same diagnostic efficacy in colorectal cancer. Conclusions: 68Ga-FAPI-04 PET/CT showed a higher diagnostic ability than 18F-FDG PET/CT in terms of diagnosing primary digestive tract cancers, especially gastric, liver, biliary tract, and pancreatic cancers. The certainty of the evidence was high due to the moderately low risk of bias and low concern regarding applicability. However, the sample size of the included studies was small and heterogeneous. More high-quality prospective studies are needed to obtain higher-quality evidence in the future. Systematic Review Registration: The systematic review was registered in PROSPERO [CRD42023402892].

Analysis of Chinese Medical Syndrome Features of Ischemic Stroke Based on Similarity of Symptoms Subgroup.

OBJECTIVE: To derive the Chinese medicine (CM) syndrome classification and subgroup syndrome characteristics of ischemic stroke patients. METHODS: By extracting the CM clinical electronic medical records (EMRs) of 7,170 hospitalized patients with ischemic stroke from 2016 to 2018 at Weifang Hospital of Traditional Chinese Medicine, Shandong Province, China, a patient similarity network (PSN) was constructed based on the symptomatic phenotype of the patients. Thereafter the efficient community detection method BGLL was used to identify subgroups of patients. Finally, subgroups with a large number of cases were selected to analyze the specific manifestations of clinical symptoms and CM syndromes in each subgroup. RESULTS: Seven main subgroups of patients with specific symptom characteristics were identified, including M3, M2, M1, M5, M0, M29 and M4. M3 and M0 subgroups had prominent posterior circulatory symptoms, while M3 was associated with autonomic disorders, and M4 manifested as anxiety; M2 and M4 had motor and motor coordination disorders; M1 had sensory disorders; M5 had more obvious lung infections; M29 had a disorder of consciousness. The specificity of CM syndromes of each subgroup was as follows. M3, M2, M1, M0, M29 and M4 all had the same syndrome as wind phlegm pattern; M3 and M0 both showed hyperactivity of Gan (Liver) yang pattern; M2 and M29 had similar syndromes, which corresponded to intertwined phlegm and blood stasis pattern and phlegm-stasis obstructing meridians pattern, respectively. The manifestations of CM syndromes often appeared in a combination of 2 or more syndrome elements. The most common combination of these 7 subgroups was wind-phlegm. The 7 subgroups of CM syndrome elements were specifically manifested as pathogenic wind, pathogenic phlegm, and deficiency pathogens. CONCLUSIONS: There were 7 main symptom similarity-based subgroups in ischemic stroke patients, and their specific characteristics were obvious. The main syndromes were wind phlegm pattern and hyperactivity of Gan yang pattern.

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

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.

Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts.

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.

Study on the TCM Syndromes Evolution and Chinese Herbal Characteristics of Type 2 Diabetes Patients with Different Courses of Disease in TCM "Heat Stage": A Real-World Study.

OBJECTIVE: The purpose of this study is to analyze and summarize the syndrome distribution, syndrome evolution, and Chinese herb medicine characteristics of T2D in heat stage. METHOD: In this study, 228 heat-stage T2D patients were divided into three groups based on the course of disease. Group 1 (the course of disease ≤5 years) included 118 patients. Group 2 (5< the course of disease ≤10 years) had 73 patients. Group 3 (the course of disease >10 years) consisted of 37 patients. The main methods used in our study were complex network community partitioning algorithms and Sankey diagram visualization, based on the clinical electronic medical record data we collected. RESULT: In the three groups, the nodes with the highest node degree are all "heat syndrome." Edge weight between "heat" and "dampness," "qi stagnation," "phlegm," "liver," and "stomach" is the largest. During the whole course of treatment, 60.17%, 63.01%, and 62.16% of the patients' syndromes in groups 1, 2, and 3, respectively, were ascribed to the heat stage all the time. The patients' syndromes in groups 1 and 2 easily transformed to the syndrome of deficiency of both qi and yin of the spleen and stomach. In group 3, 27% of the patients' syndromes were easily transformed into kidney yin deficiency and qi deficiency and blood stasis syndrome. The largest Chinese herb communities of the patients whose syndromes did not change after treatment in the three groups were all heat-clearing drugs. The proportion of blood-activating drugs in patients with syndrome changes increased significantly after treatment. CONCLUSION: (1) The basic syndrome of T2D patients in the heat stage is liver-stomach heat syndrome. (2) T2D patients in the heat stage tend to deteriorate towards the direction of qi and yin deficiency syndrome. However, the longer the course of the disease is, the more likely it is to deteriorate to the direction of kidney yin deficiency syndrome and blood stasis syndrome. (3) Drugs that can help T2D patients in the heat stage to maintain their condition stably are heat-clearing drugs represented by Coptis chinensis, which usually need to be combined with warming interior drugs such as Zingiberis Rhizoma and Pinelliae Rhizoma.

Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model.

OBJECTIVE: To predict the major comorbidities of type 2 diabetes based on the distribution characteristics of syndromes, and to explore the relationship between TCM syndromes and comorbidities of type 2 diabetes. METHODS: Based on the electronic medical record data of 3413 outpatient visits from 995 type 2 diabetes patients with comorbidities, descriptive statistical methods were used to analyze the basic characteristics of the population, the distribution characteristics of comorbidities, and TCM syndromes. A neural network model for the prediction of type 2 diabetic comorbidities based on TCM syndromes was constructed. RESULTS: Patients with TCM syndrome of blood amassment in the lower jiao were diagnosed with renal insufficiency with 95% test sensitivity. The patients with spleen deficiency combined with ascending counterflow of stomach qi and cold-damp patterns were diagnosed with gastrointestinal lesions with 92% sensitivity. The patients with TCM syndrome group of spleen heat and exuberance of heart fire were diagnosed as type 2 diabetes complicated with hypertension with a sensitivity of 91%. In addition, the prediction accuracy of combined neuropathy, heart disease, liver disease, and lipid metabolism disorder reached 70∼90% in TCM syndrome groups. CONCLUSION: The fully connected neural network model study showed that syndrome characteristics are highly correlated with type 2 diabetes comorbidities. Syndrome location is commonly in the heart, spleen, stomach, lower jiao, meridians, etc., while syndrome pattern manifests in states of deficiency, heat, phlegm, and blood stasis. The different combinations of disease location and disease pattern reflect the syndrome characteristics of different comorbidities forming the characteristic syndrome group of each comorbidity. Major comorbidities could be predicted with a high degree of accuracy through TCM syndromes. Findings from this study may have further implementations to assist with the diagnosis, treatment, and prevention of diabetic comorbidities at an early stage.

Diversity and molecular network patterns of symptom phenotypes.

Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein-protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.

Using the Symptom Patient Similarity Network to Explore the Difference between the Chinese and Western Medicine Pathways of Ischemic Stroke and its Comorbidities.

METHODS: Individualized treatment of traditional Chinese medicine (TCM) provides a theoretical basis for the study of the personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 7170 in patients with IS, a patient similarity network (PSN) with shared symptoms was constructed. Next, patient subgroups were identified using community detection methods and enrichment analyses were performed. Finally, genetic data of symptoms, herbs, and drugs were used for pathway and GO analysis to explore the characteristics of pathways of subgroups and to compare the similarities and differences in genetic pathways of herbs and drugs from the perspective of molecular pathways of symptoms. RESULTS: We identified 34 patient modules from the PSN, of which 7 modules include 98.48% of the whole cases. The 7 patient subgroups have their own characteristics of risk factors, complications, and comorbidities and the underlying genetic pathways of symptoms, drugs, and herbs. Each subgroup has the largest number of herb pathways. For specific symptom pathways, the number of herb pathways is more than that of drugs. CONCLUSION: The research of disease classification based on community detection of symptom-shared patient networks is practical; the common molecular pathway of symptoms and herbs reflects the rationality of TCM herbs on symptoms and the wide range of therapeutic targets.

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

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 Inner Canon of Huangdi (ICH, ) and Treatise on Cold Pathogenic Disease (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 Inner Canon of Huangdi and Treatise on Cold Pathogenic Diseases 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.

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

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.

Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions.

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 "Jun-Chen-Zuo-Shi" 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.

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

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.

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

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.