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Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient’s symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of a patient’s clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it is very difficult to conduct effective representation for unrecorded symptom terms in an existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM) and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from a knowledge network to effectively represent the embedding features of clinical symptom terms (especially the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyperparameters (i.e., feature embedding, feature dimension, subnetwork filter threshold, and feature fusion) demonstrate that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.

Due to the large-scale spread of COVID-19, which has a significant impact on human health and social economy, developing effective antiviral drugs for COVID-19 is vital to saving human lives. Various biomedical associations, e.g., drug-virus and viral protein-host protein interactions, can be used for building biomedical knowledge graphs. Based on these sources, large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses. To utilize the various heterogeneous biomedical associations, we proposed a fusion strategy to integrate the results of two tensor decomposition-based models (i.e., CP-N3 and ComplEx-N3). Sufficient experiments indicated that our method obtained high performance (MRR=0.2328). Compared with CP-N3, the mean reciprocal rank (MRR) is increased by 3.3% and compared with ComplEx-N3, the MRR is increased by 3.5%. Meanwhile, we explored the relationship between the performance and relationship types, which indicated that there is a negative correlation (PCC=0.446, P-value=2.26e-194) between the performance of triples predicted by our method and edge betweenness.

The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing efficient and accurate computing approaches to predict disease genes. However, owing to the sparseness and complexity of medical data, developing an efficient deep neural network model to identify disease genes remains a huge challenge. Therefore, we develop a novel deep neural network model that fuses the multi-view features of phenotypes and genotypes to identify disease genes (termed PDGNet). Our model integrated the multi-view features of diseases and genes and leveraged the feedback information of training samples to optimize the parameters of deep neural network and obtain the deep vector features of diseases and genes. The evaluation experiments on a large data set indicated that PDGNet obtained higher performance than the state-of-the-art method (precision and recall improved by 9.55 and 9.63 percent). The analysis results for the candidate genes indicated that the predicted genes have strong functional homogeneity and dense interactions with known genes. We validated the top predicted genes of Parkinson's disease based on external curated data and published medical literatures, which indicated that the candidate genes have a huge potential to guide the selection of causal genes in the 'wet experiment'. The source codes and the data of PDGNet are available at https://github.com/yangkuoone/PDGNet.

Most ensemble clustering algorithms use K-means to generate base clustering, but the result of base clustering is not good. And most ensemble clustering algorithms ignore the diversity of base clustering, treat base clustering equally, and generate the co-association matrix on the samples. When the number of samples or integration scale is large, the computational burden increases significantly. To solve the above problems, an ensemble clustering algorithm based on weighted super cluster (ECWSC) is proposed. This algorithm combines random selection with K-means selection to obtain landmarks sampling, and uses spectral clustering algorithm for landmarks to get the clustering result. Then, the samples are mapped to the nearest landmark points to get the base clustering. On this basis, the uncertainty of the base clustering is calculated, and the corresponding weight is given. Then the co-association matrix based on weighted super cluster is obtained by weighted method, and the integration result is obtained by using hierarchical clustering algorithm. 7 real datasets and 4 artificial datasets are selected as experimental datasets to verify the accuracy, robustness and time complexity of the methods. Experimental results show that this algorithm can effectively improve the ensemble clustering effect.

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.

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.

Qizhu Tangshen Formula (QZTS) has been shown therapeutic effects on diabetic kidney disease (DKD). However, to date, the pharmacological mechanisms remain vague.To explore the underlying mechanisms of QZTS in treating DKD using network pharmacology, machine learning, molecular docking and experimental assessment.First, we found that QZTS improved glycolipid metabolism disorder, decreased proteinuria and alleviated kidney tissue injury in DKD model KKAy mice. Then, by integrating multiple databases, a total of 96 targets of 74 active compounds in QZTS and 759 DKD-related genes were acquired. Next, we identified 13 hub targets of QZTS in DKD by three rank algorithms, including functional similarity, topological similarity and shortest path. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that the pathways mainly centered on the processes of glycolipid metabolism disorder, inflammation and angiogenesis. Among them, VEGF signaling pathway was significantly enriched. Molecular docking showed that key active compounds of QZTS all had relatively good binding affinity with predicted hub targets. Finally, animal experiments found that QZTS significantly inhibited the secretion of plasma VEGF and downregulated the protein and mRNA expression levels of AKT, p38MAPK and VEGFR2.Our results indicated that QZTS treated DKD via multiple targets and pathways and the VEGF signaling pathway may be highly involved in this process.

Coronavirus disease 2019 (COVID-19) is now pandemic worldwide and has heavily overloaded hospitals in Wuhan City, China during the time between late January and February. We reported the clinical features and therapeutic characteristics of moderate COVID-19 cases in Wuhan that were treated via the integration of traditional Chinese medicine (TCM) and Western medicine. We collected electronic medical record (EMR) data, which included the full clinical profiles of patients, from a designated TCM hospital in Wuhan. The structured data of symptoms and drugs from admission notes were obtained through an information extraction process. Other key clinical entities were also confirmed and normalized to obtain information on the diagnosis, clinical treatments, laboratory tests, and outcomes of the patients. A total of 293 COVID-19 inpatient cases, including 207 moderate and 86 (29.3%) severe cases, were included in our research. Among these cases, 238 were discharged, 31 were transferred, and 24 (all severe cases) died in the hospital. Our COVID-19 cases involved elderly patients with advanced ages (57 years on average) and high comorbidity rates (61%). Our results reconfirmed several well-recognized risk factors, such as age, gender (male), and comorbidities, as well as provided novel laboratory indications (e.g., cholesterol) and TCM-specific phenotype markers (e.g., dull tongue) that were relevant to COVID-19 infections and prognosis. In addition to antiviral/antibiotics and standard supportive therapies, TCM herbal prescriptions incorporating 290 distinct herbs were used in 273 (93%) cases. The cases that received TCM treatment had lower death rates than those that did not receive TCM treatment (17/273 = 6.2% vs. 7/20= 35%, P = 0.0004 for all cases; 17/77= 22% vs. 7/9= 77.7%, P = 0.002 for severe cases). The TCM herbal prescriptions used for the treatment of COVID-19 infections mainly consisted of Pericarpium Citri Reticulatae, Radix Scutellariae, Rhizoma Pinellia, and their combinations, which reflected the practical TCM principles (e.g., clearing heat and dampening phlegm). Lastly, 59% of the patients received treatment, including antiviral, antibiotics, and Chinese patent medicine, before admission. This situation might have some effects on symptoms, such as fever and dry cough. By using EMR data, we described the clinical features and therapeutic characteristics of 293 COVID-19 cases treated via the integration of TCM herbal prescriptions and Western medicine. Clinical manifestations and treatments before admission and in the hospital were investigated. Our results preliminarily showed the potential effectiveness of TCM herbal prescriptions and their regularities in COVID-19 treatment.

The coronavirus disease 2019 (COVID-19) spreads rapidly across the globe, seriously threatening the health of people all over the world. To reduce the diagnostic pressure of front-line doctors, an accurate and automatic lesion segmentation method is highly desirable in clinic practice.Many proposed two-dimensional (2D) methods for sliced-based lesion segmentation cannot take full advantage of spatial information in the three-dimensional (3D) volume data, resulting in limited segmentation performance. Three-dimensional methods can utilize the spatial information but suffer from long training time and slow convergence speed. To solve these problems, we propose an end-to-end hybrid-feature cross fusion network (HFCF-Net) to fuse the 2D and 3D features at three scales for the accurate segmentation of COVID-19 lesions.The proposed HFCF-Net incorporates 2D and 3D subnets to extract features within and between slices effectively. Then the cross fusion module is designed to bridge 2D and 3D decoders at the same scale to fuse both types of features. The module consists of three cross fusion blocks, each of which contains a prior fusion path and a context fusion path to jointly learn better lesion representations. The former aims to explicitly provide the 3D subnet with lesion-related prior knowledge, and the latter utilizes the 3D context information as the attention guidance of the 2D subnet, which promotes the precise segmentation of the lesion regions. Furthermore, we explore an imbalance-robust adaptive learning loss function that includes image-level loss and pixel-level loss to tackle the problems caused by the apparent imbalance between the proportions of the lesion and non-lesion voxels, providing a learning strategy to dynamically adjust the learning focus between 2D and 3D branches during the training process for effective supervision.Extensive experiments conducted on a publicly available dataset demonstrate that the proposed segmentation network significantly outperforms some state-of-the-art methods for the COVID-19 lesion segmentation, yielding a Dice similarity coefficient of 74.85%. The visual comparison of segmentation performance also proves the superiority of the proposed network in segmenting different-sized lesions.In this paper, we propose a novel HFCF-Net for rapid and accurate COVID-19 lesion segmentation from chest computed tomography volume data. It innovatively fuses hybrid features in a cross manner for lesion segmentation, aiming to utilize the advantages of 2D and 3D subnets to complement each other for enhancing the segmentation performance. Benefitting from the cross fusion mechanism, the proposed HFCF-Net can segment the lesions more accurately with the knowledge acquired from both subnets.

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.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally. The effect of Chinese medicine (CM) on mortality during acute exacerbation of COPD is unclear. We evaluated the real-world effectiveness of add-on personalized CM in hospitalized COPD patients with acute exacerbation.This is a retrospective cohort study with new-user design. All electronic medical records of hospitalized adult COPD patients (n = 4781) between July 2011 and November 2019 were extracted. Personalized CM exposure was defined as receiving CM that were prescribed, and not in a fixed form and dose at baseline. A 1:1 matching control cohort was generated from the same source and matched by propensity score. Primary endpoint was mortality. Multivariable Cox regression models were used to estimate the hazard ratio (HR) adjusting the same set of covariates (most prevalent with significant inter-group difference) used in propensity score calculation. Secondary endpoints included the change in hematology and biochemistry, and the association between the use of difference CMs and treatment effect. The prescription pattern was also assessed and the putative targets of the CMs on COPD was analyzed with network pharmacology approach.4325 (90.5%) patients were included in the analysis. The mean total hospital stay was 16.7 ± 11.8 days. In the matched cohort, the absolute risk reduction by add-on personalized CM was 5.2% (3.9% vs 9.1%). The adjusted HR of mortality was 0.13 (95% CI: 0.03 to 0.60, p = 0.008). The result remained robust in the sensitivity analyses. The change in hematology and biochemistry were comparable between groups. Among the top 10 most used CMs, Poria (Fu-ling), Citri Reticulatae Pericarpium (Chen-pi) and Glycyrrhizae Radix Et Rhizoma (Gan-cao) were associated with significant hazard reduction in mortality. The putative targets of the CM used in this cohort on COPD were related to Jak-STAT, Toll-like receptor, and TNF signaling pathway which shares similar mechanism with a range of immunological disorders and infectious diseases.Our results suggest that add-on personalized Chinese medicine was associated with significant mortality reduction in hospitalized COPD patients with acute exacerbation in real-world setting with minimal adverse effect on liver and renal function. Further randomized trials are warranted.

One of the most difficult problems that hinder the development and application of herbal medicine is how to illuminate the global effects of herbs on the human body. Currently, the chemo-centric network pharmacology methodology regards herbs as a mixture of chemical ingredients and constructs the ‘herb-compound-target-disease’ connections based on bioinformatics methods, to explore the pharmacological effects of herbal medicine. However, this approach is severely affected by the complexity of the herbal composition. Alternatively, gene-expression profiles induced by herbal treatment reflect the overall biological effects of herbs and are suitable for studying the global effects of herbal medicine. Here, we develop an online transcriptome-based multi-scale network pharmacology platform (TMNP) for exploring the global effects of herbal medicine. Firstly, we build specific functional gene signatures for different biological scales from molecular to higher tissue levels. Then, specific algorithms are designed to measure the correlations of transcriptional profiles and types of gene signatures. Finally, TMNP uses pharmacotranscriptomics of herbal medicine as input and builds associations between herbs and different biological scales to explore the multi-scale effects of herb medicine. We applied TMNP to a single herb Astragalus membranaceus and Xuesaitong injection to demonstrate the power to reveal the multi-scale effects of herbal medicine. TMNP integrating herbal medicine and multiple biological scales into the same framework, will greatly extend the conventional network pharmacology model centering on the chemical components, and provide a window for systematically observing the complex interactions between herbal medicine and the human body. TMNP is available at http://www.bcxnfz.top/TMNP.

The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks.

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.

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 .

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.

Introduction Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi-morbidity/treatment complexity. Some headway has been made to identify genetic and non-genetic risk factors for T2DM. However longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi-morbidity. Methods This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre- and post-T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n=237 vs matched non-T2DM controls n=44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year. Results For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis a greater indication of ischaemic heart disease proportions was observed. Post-T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure. Conclusion These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially-related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores.

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.

The rapid development of the times has created a new realistic context for the cultivation of college students' struggle spirit. Today, with the rapid development of the times, we must carefully examine the value of the era of the cultivation of the spirit of struggle for college students in the new era, and fully understand the serious challenges that the in-depth integration of consumerism and individualism has brought to the cultivation of the spirit of struggle for college students. This article uses research methods of literature and logical analysis to start from the aspects of content cultivation, emotional stimulation, discourse guidance, and optimization of expression forms to stimulate the spirit of struggle of college students. For this reason, we should pay attention to the influencing factors in the cultivation environment, strengthen the value guidance of the cultivation of the spirit of struggle of college students in the new era; innovate and cultivate the discourse, firmly grasp the right to speak in the cultivation of the spirit of struggle of college students in the new era, and continuously promote and optimize the cultivation of the spirit of struggle of college students in the new era. Education guides universities to become fighters in the new era and strive for the great rejuvenation of the Chinese nation.

Mobile Internet technology is constantly improving, and its impact on people's daily life style continues to deepen. The rapid development of artificial intelligence technology has brought new opportunities and challenges to mankind, especially having a profound impact on education. This article uses qualitative research methods to conduct research and finds that the advent of the artificial intelligence era has caused major changes in education. Artificial intelligence technology provides new ideas for the development of education and points out new directions. Education and artificial intelligence are new cores. The integrated development of technology can provide data support for the scientific management of education, optimize the relationship between teachers and students, and promote personalized learning. Grabbing valuable information, establishing an education and teaching information resource database, and establishing an education and teaching effect evaluation mechanism based on big data are the countermeasures for education in the era of artificial intelligence.

The times are developing rapidly, and the ideological field must keep pace with the development of the times. The ideological and political work of colleges and universities shoulders the important task of cultivating qualified builders and reliable successors of socialism. It should adapt to the laws of the development of the times and realize the youth in an orderly manner, ideological and political leadership of college students. This article uses qualitative research methods to accurately study and judge the ideological characteristics and current status of values of young college students, deeply integrate new ideas and new theories into the entire process of ideological and political education in colleges and universities, and solve the problem with the supply-side structural reform of the education system. The demand-side issues of ideological and political guidance of young college students explore new and effective ways of educating people, and then realize the new mechanism of ideological and political work in colleges and universities.

As a growing trend in current pharmacology research and an important medical application in network science research, network pharmacology has become an indispensable complement to traditional pharmacology research with the immense accumulation and integration of large-scale pharmacology and disease molecular network data. In addition to emerging new methods and technologies, a large number of analytical techniques and methods, such as network analysis and molecular functional analysis have matured into related software or programs and are available for researchers to use for free or available as open source, which is an important factor and condition that has helped network pharmacology’s robust and rapid development. From the perspective of information processing and computer systems, this chapter classifies and introduces commonly used network pharmacology software by refining the overall functional flow of network pharmacology-related software or programs and illustrates demonstrative application examples by combining actual data processing, analysis, and visualization operations. The practical steps and contents in this chapter, combined with the theoretical analysis, analysis methods, and research cases of network pharmacology in other chapters, can provide researchers or students with relevant software tools and practical operation methods that can be used for reference, as well as provide rapid and convenient software tool selection and practical guidance for actual research on network pharmacology.

Institutions of higher learning shoulder the historical mission and responsibility of talent training, and scientific research and cultivation are the fundamental embodiment and basic follow of the new era of "establishing moralities and cultivating people". This article uses research methods of literature and logical analysis to systematically analyze the spirit of scientific research and cultivation in colleges and universities in the new era, and proposes to establish a firm awareness of cultivating people and fulfill responsibilities; strengthen value guidance and insist on positive indoctrination; create education the specific methods and measures to sublimate rational cognition by exerting edification effect and focusing on practical experience, hoping to provide useful reference and help.

The modernization of the national governance system and governance capacity has been continuously clarified from theoretical connotations to practical paths, which has pointed out the direction and put forward requirements for the modernization of the governance system and governance capacity of colleges and universities. This article uses qualitative research methods and finds that at this stage, the value connotation of higher education governance system and governance capacity building is still vague, the institutional system is in urgent need of new construction, and the power position is obviously weakened. This article proposes a multi-path transformation method such as constructing an orderly operation governance system to enhance the autonomous management capacity of colleges and universities, and building an internal and external linkage mechanism for colleges and universities to give play to the governance wisdom of multiple subjects. It is hoped that it can provide benefits for the construction of college governance systems and governance capacity for reference.

Aim: To explore the distribution and correlation of comorbidities in Chinese adult patients with type 2 diabetes mellitus(T2DM) by using real-world clinical medical record data. Methods: This study is a retrospective study, screening data from the previous medical records using integrated traditional Chinese and western medicine in the treatment of diabetes. In this study, descriptive statistic, association rules, complex network and other data mining methods were used to extract and analyze the associated data of diabetes mellitus comorbidities. Results: A total of 5227 clinical records of patients with type 2 diabetes were included in this study, and the top 10 comorbidities were identified. It was found that there was a correlation among concomitant diseases, and hypertension was most strongly associated with other concomitant diseases (degree > 248). It was also found that the distribution of comorbidities was closely related to age, and the distribution showed a certain rule, which is consistent with the understanding of the pathogenesis evolution of diabetes in TCM. Conclusion: Comorbidities of diabetes show associations with different intensity, and there are obviously characteristic disease groups in different age stages. Based on the results of this study, patients with diabetes can purposefully improve their awareness of preventing the related diseases. And it also provides useful reference for the prevention and treatment of diabetes and its associated diseases.

The diagnosis and treatment of traditional Chinese medicine (TCM) are formed based on the differentiation of syndromes and symptoms. Symptom management is always the core task of nursing science. Connotation between TCM symptoms and Modern medicine (MM) symptoms are obvious different, especially tongue and pulse symptoms of TCM. However, the underlying molecular mechanisms of most TCM symptoms remain unclear. Here, we developed a network-based framework to predict candidate genes of TCM symptoms (called PTsGene) and construction a high-quality set of TCM symptom-gene associations. Experimental results indicated that PTsGene performed significantly better than the baseline algorithms. The reliability of the candidate genes of symptoms (containing one of typical symptoms of COVID-19, fever) were validated by the analysis of functional homogeneity, molecular co-expression, and recently published literatures. Finally, a high-quality set of TCM symptom-gene associations is constructed to promote the mechanism developments of TCM symptoms. Prediction and construction for reliable TCM symptom-gene associations are valuable for uncovering the underlying molecular mechanisms of TCM symptoms. Our TCM symptom-gene associations deliver a highly insightful data sources for researchers both from basic and clinical settings of precision healthcare.

Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.

To obtain the subtypes of the clinical hypertension population based on symptoms and to explore the relationship between hypertension and comorbidities. The data set was collected from the Chinese medicine (CM) electronic medical records of 33,458 hypertension inpatients in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between July 2014 and May 2017. Then, a hypertension disease comorbidity network (HDCN) was built to investigate the complicated associations between hypertension and their comorbidities. Moreover, a hypertension patient similarity network (HPSN) was constructed with patients’ shared symptoms, and 7 main hypertension patient subgroups were identified from HPSN with a community detection method to exhibit the characteristics of clinical phenotypes and molecular mechanisms. In addition, the significant symptoms, diseases, CM syndromes and pathways of each main patient subgroup were obtained by enrichment analysis. The significant symptoms and diseases of these patient subgroups were associated with different damaged target organs of hypertension. Additionally, the specific phenotypic features (symptoms, diseases, and CM syndromes) were consistent with specific molecular features (pathways) in the same patient subgroup. The utility and comprehensiveness of disease classification based on community detection of patient networks using shared CM symptom phenotypes showed the importance of hypertension patient subgroups.

Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms need be further elucidated. Herbal drugs have been widely used for controlling various pain disorders. The systematic integration of pain herbal data resources might be promising to help investigate the molecular mechanisms of pain phenotypes. Here, we integrated large-scale bibliographic literatures and well-established data sources to obtain high-quality pain relevant herbal data (i.e. 426 pain related herbs with their targets). We used machine learning method to identify three distinct herb categories with their specific indications of symptoms, targets and enriched pathways, which were characterized by the efficacy of treatment to the chronic cough related neuropathic pain, the reproduction and autoimmune related pain, and the cancer pain, respectively. We further detected the novel pathophysiological mechanisms of the pain subtypes by network medicine approach to evaluate the interactions between herb targets and the pain disease modules. This work increased the understanding of the underlying molecular mechanisms of pain subtypes that herbal drugs are participating and with the ultimate aim of developing novel personalized drugs for pain disorders.

This study investigated whether Panax notoginseng saponins (PNS) reduced atherosclerotic lesion formation in apolipoprotein E knockout (ApoE-KO) mice and illustrated the potential mechanism for a network pharmacology approach. Pharmacodynamics studies on ApoE-KO mice with atherosclerosis (AS) showed that PNS generated an obvious anti-AS action. Then, we explored the possible mechanisms underlying its anti-AS effect using the network pharmacology approach. The main chemical components and their targets of PNS were collected from TCMSP public database and SymMap. The STRING v11.0 was used to establish the protein-protein interactions of PNS. Furthermore, the Gene Ontology (GO) function and KEGG pathways were analyzed using STRING to investigate the possible mechanisms involved in the anti-AS effect of PNS. The predicted results showed that 27 potential targets regulated by DSLHG were related to AS, including ACTA2, AKT1, BCL2, and BDNF. Mechanistically, the anti-AS effect of PNS was exerted by interfering with multiple signaling pathways, such as AGE-RAGE signaling pathway, fluid shear stress and atherosclerosis, and TNF signaling pathway. Network analysis showed that PNS could generate the anti-AS action by affecting multiple targets and multiple pathways and provides a novel basis to clarify the mechanisms of anti-AS of PNS.

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, 黄帝内经) and Treatise on Cold Pathogenic Disease (TCPD, 伤寒论), 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.

This study investigated whether Panax notoginseng saponins (PNS) reduced atherosclerotic lesion formation in apolipoprotein E knockout (ApoE-KO) mice and illustrated the potential mechanism for a network pharmacology approach. Pharmacodynamics studies on ApoE-KO mice with atherosclerosis (AS) showed that PNS generated an obvious anti-AS action. Then, we explored the possible mechanisms underlying its anti-AS effect using the network pharmacology approach. The main chemical components and their targets of PNS were collected from TCMSP public database and SymMap. The STRING v11.0 was used to establish the protein-protein interactions of PNS. Furthermore, the Gene Ontology (GO) function and KEGG pathways were analyzed using STRING to investigate the possible mechanisms involved in the anti-AS effect of PNS. The predicted results showed that 27 potential targets regulated by DSLHG were related to AS, including ACTA2, AKT1, BCL2, and BDNF. Mechanistically, the anti-AS effect of PNS was exerted by interfering with multiple signaling pathways, such as AGE-RAGE signaling pathway, fluid shear stress and atherosclerosis, and TNF signaling pathway. Network analysis showed that PNS could generate the anti-AS action by affecting multiple targets and multiple pathways and provides a novel basis to clarify the mechanisms of anti-AS of PNS.

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.

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.

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy., Highlights • The International Classification of Diseases (ICD) lags behind the current molecular characteristics of disease. • We quantified the limitations (specificity and blurred boundary) of ICD with integrated phenotypic and molecular profiles. • An integrative disease network integrating phenotypic and genotypic profiles proposes a refined disease category framework. Disease taxonomy is one of the foundations of medical science and healthcare solutions. The most widely used disease taxonomy in clinical settings is the International Classification of Diseases (ICD), a system established >100 years ago and maintained by the World Health Organization to track disease incidence. It is well recognized that ICD, which is based on clinical observations, largely lags behind the molecular achievements of this medical big data era. We quantified the limitations of ICD using integrated phenotypic and molecular profiles and proposed a refined disease taxonomy with possible applications for precision medicine.

A large amount of available protein-protein interaction (PPI) data has been generated by high-throughput experimental techniques. Uncovering functional modules from PPI networks will help us better understand the underlying mechanisms of cellular functions. Numerous computational algorithms have been designed to identify functional modules automatically in the past decades. However, most community detection methods (non-overlapping or overlapping types) are unsupervised models, which cannot incorporate the well-known protein complexes as a priori. The authors propose a novel semi-supervised model named pairwise constrains nonnegative matrix tri-factorisation (PCNMTF), which takes full advantage of the well-known protein complexes to find overlapping functional modules based on protein module indicator matrix and module correlation matrix simultaneously from PPI networks. PCNMTF determinately models and learns the mixed module memberships of each protein by considering the correlation among modules simultaneously based on the non-negative matrix tri-factorisation. The experiment results on both synthetic and real-world biological networks demonstrate that PCNMTF gains more precise functional modules than that of state-of-the-art methods.