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

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

Investigation into the Influence of Physician for Treatment Based on Syndrome Differentiation

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

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

IntroductionAlthoug 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.MethodsIn 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.ResultsA 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.Conclusions68Ga-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 RegistrationThe systematic review was registered in PROSPERO [CRD42023402892]

Heterogeneous network propagation for herb target identification

Abstract 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

A multi‐modal clustering method for traditonal Chinese medicine clinical data via media convergence

Abstract Media convergence is a media change led by technological innovation. Applying media convergence technology to the study of clustering in Chinese medicine can significantly exploit the advantages of media fusion. Obtaining consistent and complementary information among multiple modalities through media convergence can provide technical support for clustering. This article presents an approach based on Media Convergence and Graph convolution Encoder Clustering (MCGEC) for traditonal Chinese medicine (TCM) clinical data. It feeds modal information and graph structure from media information into a multi‐modal graph convolution encoder to obtain the media feature representation learnt from multiple modalities. MCGEC captures latent information from various modalities by fusion and optimises the feature representations and network architecture with learnt clustering labels. The experiment is conducted on real‐world multi‐modal TCM clinical data, including information like images and text. MCGEC has improved clustering results compared to the generic single‐modal clustering methods and the current more advanced multi‐modal clustering methods. MCGEC applied to TCM clinical datasets can achieve better results. Integrating multimedia features into clustering algorithms offers significant benefits compared to single‐modal clustering approaches that simply concatenate features from different modalities. It provides practical technical support for multi‐modal clustering in the TCM field incorporating multimedia features

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, 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

Additional file 3: of Heterogeneous network propagation for herb target identification

– KEGG_protein_pathway.xls. 16,162 protein-pathway associations between 4794 proteins and 244 pathways were collected from KEGG database. (XLS 896 kb

Additional file 2: of Heterogeneous network propagation for herb target identification

– CHPA_herb_efficacy.xls. 3487 herb-efficacy associations between 742 herbs and 360 efficacies were collected from the Chinese pharmacopoeia (CHPA, 2015 edition). (XLS 200 kb

Additional file 1: of Heterogeneous network propagation for herb target identification

– HIT_herb_target.xls. 23,453 herb-target associations between 1016 herbs and 1214 targets were collected and integrated from the HIT database. (XLS 1200 kb

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

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