Dataset Information

FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.

ABSTRACT: The use of herbs to treat various human diseases has been recorded for thousands of years. In Asia's current medical system, numerous herbal formulas have been repeatedly verified to confirm their effectiveness in different periods, which is a great resource for drug innovation and discovery. Through the mining of these clinical effective formulas by network pharmacology and bioinformatics analysis, important biologically active ingredients derived from these natural products might be discovered. As modern medicine requires a combination of multiple drugs for the treatment of complex diseases, previously clinical formulas are also combinations of various herbs according to the main causes and accompanying symptoms. However, the herbs that play a major role in the treatment of diseases are always unclear. Therefore, how to rank each herb's relative importance and determine the core herbs, is the first step to assisting herb selection for active ingredients discovery. To solve this problem, we built the platform FangNet, which ranks all herbs on their relative topological importance using the PageRank algorithm, based on the constructed symptom-herb network from a collection of clinical empirical prescriptions. Three types of herb hidden knowledge, including herb importance rank, herb-herb co-occurrence, and associations to symptoms, were provided in an interactive visualization. Moreover, FangNet has designed role-based permission for teams to store, analyze, and jointly interpret their clinical formulas, in an easy and secure collaboration environment, aiming at creating a central hub for massive symptom-herb connections. FangNet can be accessed at http://fangnet.org or http://fangnet.herb.ac.cn.

SUBMITTER: Bu D

PROVIDER: S-EPMC7753081 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.

Bu Dechao D Xia Yan Y Zhang JiaYuan J Cao Wanchen W Huo Peipei P Wang Zhihao Z He Zihao Z Ding Linyi L Wu Yang Y Zhang Shan S Gao Kai K Yu He H Liu Tiegang T Ding Xia X Gu Xiaohong X Zhao Yi Y

Computational and structural biotechnology journal 20201204

The use of herbs to treat various human diseases has been recorded for thousands of years. In Asia's current medical system, numerous herbal formulas have been repeatedly verified to confirm their effectiveness in different periods, which is a great resource for drug innovation and discovery. Through the mining of these clinical effective formulas by network pharmacology and bioinformatics analysis, important biologically active ingredients derived from these natural products might be discovered ...[more]

PMID: 33363710

Similar Datasets

Project description:BackgroundToxicogenomics studies often profile gene expression from assays involving multiple doses and time points. The dose- and time-dependent pattern is of great importance to assess toxicity but computational approaches are lacking to effectively utilize this characteristic in toxicity assessment. Topic modeling is a text mining approach, but may be used analogously in toxicogenomics due to the similar data structures between text and gene dysregulation.ResultsTopic modeling was applied to a very large toxicogenomics dataset containing microarray gene expression data from >15,000 samples associated with 131 drugs tested in three different assay platforms (i.e., in vitro assay, in vivo repeated dose study and in vivo single dose experiment) with a design including multiple doses and time points. A set of "topics" which each consist of a set of genes was determined, by which the varying sensitivity of three assay systems was observed. We found that the drug-dependent effect was more pronounced in the two in vivo systems than the in vitro system, while the time-dependent effect was most strongly reflected in the in vitro system followed by the single dose study and lastly the repeated dose experiment. The dose-dependent effect was similar across three assay systems. Although the results indicated a challenge to extrapolate the in vitro results to the in vivo situation, we did notice that, for some drugs but not for all the drugs, the similarity in gene expression patterns was observed across all three assay systems, indicating a possibility of using in vitro systems with careful designs (such as the choice of dose and time point), to replace the in vivo testing strategy. Nonetheless, a potential to replace the repeated dose study by the single-dose short-term methodology was strongly implied.ConclusionsThe study demonstrated that text mining methodologies such as topic modeling provide an alternative method compared to traditional means for data reduction in toxicogenomics, enhancing researchers' capabilities to interpret biological information.

Project description:In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident.We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship.Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network could be used to identify likely disease associations.We analyzed the human protein interaction network and its relationship to disease and found that both the number of interactions with other proteins and the disease relationship of neighboring proteins helped to determine whether a protein had a relationship to disease. Our classifier predicted many proteins with no annotated disease association to be disease-related, which indicated that these proteins have network characteristics that are similar to disease-related proteins and may therefore have disease associations not previously identified. By performing a post-processing step after the prediction, we were able to identify evidence in literature supporting this possibility. This method could provide a useful filter for experimentalists searching for new candidate protein targets for drug repositioning and could also be extended to include other network and data types in order to refine these predictions.

Project description:BackgroundGiven the significant impact on public health and drug development, drug safety has been a focal point and research emphasis across multiple disciplines in addition to scientific investigation, including consumer advocates, drug developers and regulators. Such a concern and effort has led numerous databases with drug safety information available in the public domain and the majority of them contain substantial textual data. Text mining offers an opportunity to leverage the hidden knowledge within these textual data for the enhanced understanding of drug safety and thus improving public health.MethodsIn this proof-of-concept study, topic modeling, an unsupervised text mining approach, was performed on the LiverTox database developed by National Institutes of Health (NIH). The LiverTox structured one document per drug that contains multiple sections summarizing clinical information on drug-induced liver injury (DILI). We hypothesized that these documents might contain specific textual patterns that could be used to address key DILI issues. We placed the study on drug-induced acute liver failure (ALF) which was a severe form of DILI with limited treatment options.ResultsAfter topic modeling of the "Hepatotoxicity" sections of the LiverTox across 478 drug documents, we identified a hidden topic relevant to Hy's law that was a widely-accepted rule incriminating drugs with high risk of causing ALF in humans. Using this topic, a total of 127 drugs were further implicated, 77 of which had clear ALF relevant terms in the "Outcome and management" sections of the LiverTox. For the rest of 50 drugs, evidence supporting risk of ALF was found for 42 drugs from other public databases.ConclusionIn this case study, the knowledge buried in the textual data was extracted for identification of drugs with potential of causing ALF by applying topic modeling to the LiverTox database. The knowledge further guided identification of drugs with the similar potential and most of them could be verified and confirmed. This study highlights the utility of topic modeling to leverage information within textual drug safety databases, which provides new opportunities in the big data era to assess drug safety.

Project description:BackgroundSix Chinese herb formulas, namely, the Weijing decoction (WJ), the Maxingshigan decoction (MXSG), the Yuebijiabanxia decoction (YBBX), the Qingqihuatan decoction (QQHT), the Dingchuan decoction (DC) and the Sangbaipi decoction (SBP), are commonly used, along with routine pharmacotherapy, to manage the acute exacerbation of chronic obstructive pulmonary disease (AECOPD). In this study, we conducted a systematic review to summarize the efficacy of these six formulas, and we also conducted a network meta-analysis (NMA) to rank these formulas.MethodsWe searched five English databases and four Chinese databases, with dates ranging from the starting dates of these databases to December 2016. Randomized controlled trials that evaluated any of the six Chinese herb formulas combined with the use of pharmacotherapy for AECOPD were identified.ResultsFifty-five studies involving 4560 participants were included. The pairwise meta-analyses showed that WJ and QQHT had superior effects on the improvement of lung function (forced expiratory volume in 1 seconds; FEV1) (mean difference (MD): 0.25, 95% confidence interval (CI): 0.19-0.30 and 0.34, 95%CI: 0.10-0.58). MXSG, WJ and QQHT were found to be more effective for improving arterial blood gases (PaO2 and PaCO2). In terms of effective rates, all of these formulas had additional favourable effects compared to routine pharmacotherapy. The results of the NMA analyses indicated that only MXSG showed superior add-on effects for the improvement of FEV1 (MD: 0.37, 95% credible interval (CrI): 0.03-0.72). Most of the formulas combined with routine pharmacotherapy were superior to pharmacotherapy alone for the improvement of arterial blood gases and effective rates. The ranking tests suggested that QQHT and MXSG combined with routine pharmacotherapy might be optimal options for the treatment of AECOPD.ConclusionsThis NMA indicated that QQHT and MXSG might be more effective treatment regimens for AECOPD. Further well-designed studies that specifically examine the direct comparisons of these formulas are needed to support our conclusions.

Dataset Information

FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.

Publications

FangNet: Mining herb hidden knowledge from TCM clinical effective formulas using structure network algorithm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets