Integrated data mining and network pharmacology to discover a novel traditional Chinese medicine prescription against diabetic retinopathy and reveal its mechanism

Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.

Exploring the medication pattern and mechanism of action of traditional Chinese medicine in treating polycystic ovary syndrome with kidney deficiency and blood stasis based on data mining and network pharmacology

Background:Using network pharmacology to explore the potential molecular mechanism of traditional Chinese medicine in treating polycystic ovary syndrome(PCOS)with kidney deficiency and blood stasis syndrome.Method:Collect the related literature materials of PCOS with kidney deficiency and blood stasis syndrome treated by traditional Chinese medicine in four databases in recent ten years,extract the information of prescriptions and complete the frequency analysis.Traditional Chinese Medicine Systems Pharmacology Database was used to screen out the effective components.Use Online Mendelian Inheritance in Man and other databases to screen PCOS disease targets.The intersection targets obtained by clustering prescription and PCOS disease targets were submitted to STRING database for protein-protein interaction network analysis,and Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways were analysed by Metascape.Result:There are 155 kinds of traditional Chinese medicines used in the literature.The most commonly utilized ones are Cuscutae Semen,Angelicae Sinensis Radix,and Rehmanniae Radix Praeparata.The results of the cluster analysis indicated that the plants most commonly found throughout the prescription were Leonuri Herba,Lycopi Herba,Dipsaci Radix,etc.GO results show that biological processes include cell reaction to organic nitrogen compounds and cell reaction to nitrogen compounds.The functional display of GO molecule includes cytokine receptor binding,signal receptor regulator activity and so on.Kyoto Encyclopedia of Genes and Genomes results show that the possible mechanisms of action are cancer pathway,an endocrine resistance signal pathway.Conclusion:Through data mining,the cluster prescription for PCOS with kidney deficiency and blood stasis syndrome is Leonuri Herba,Lycopi Herba,Dipsaci Radix,etc.The network pharmacology research of cluster prescription shows that the main drug components for treating PCOS with kidney deficiency and blood stasis syndrome are quercetin,kaempferol,luteolin,tanshinone IIA,etc.,which act on PTGS2,NCOA2,and other targets,and treat PCOS with kidney deficiency and blood stasis syndrome through cancer and endocrine resistance.

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS

In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore,"Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3×107 data points),and knowledge is found effectively.

Data Mining and Neural Network Techniques in Case Based System

This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.

Temporal Data Mining Using Genetic Algorithm and Neural Network——A Case Study of Air Pollutant Forecasts

This paper integrates genetic algorithm and neura l network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temp oral research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intellig ent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and tempor al information analyst (ASIA) system which then is fully utilized in the existin g GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual tempo ral analysis framework.

Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence” and “support” is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support” is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.

Theoretical Research on Novel Data Mining Algorithm based on Fuzzy Clustering Theory and Deep Neural Network

With the progress of computer technology, data mining has become a hot research area in the computer science community. In this paper, we undertake theoretical research on the novel data mining algorithm based on fuzzy clustering theory and deep neural network. The focus of data mining in seeking the visualization methods in the process of data mining, knowledge discovery process can be users to understand, to facilitate human-computer interaction in knowledge discovery process. Inspired by the brain structure layers, neural network researchers have been trying to multilayer neural network research. The experiment result shows that out algorithm is effective and robust.

A systematic study of Erzhu Erchen decoction against damp-heat internalized type 2 diabetes based on data mining and experimental verification

Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.

A Novel Operational Partition between Neural Network Classifiers on Vulnerability to Data Mining Bias

It is difficult if not impossible to appropriately and effectively select from among the vast pool of existing neural network machine learning predictive models for industrial incorporation or academic research exploration and enhancement. When all models outperform all the others under disparate circumstances, none of the models do. Selecting the ideal model becomes a matter of ill-supported opinion ungrounded on the extant real world environment. This paper proposes a novel grouping of the model pool grounded along a non-stationary real world data line into two groups: Permanent Data Learning and Reversible Data Learning. This paper further proposes a novel approach towards qualitatively and quantitatively demonstrating their significant differences based on how they alternatively approach dynamic and raw real world data vs static and prescient data mining biased laboratory data. The results across 2040 separate simulation runs using 15,600 data points in realistically operationally controlled data environments show that the two-group division is effective and significant with clear qualitative, quantitative and theoretical support. Results across the empirical and theoretical spectrum are internally and externally consistent yet demonstrative of why and how this result is non-obvious.

Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.

National patent Chinese herbal compound for parkinson's disease based on data mining and network pharmacology analysis

Objective:To analyze the dosing pattern and mechanism of herbal compound patents for the treatment of Parkinson's disease using data mining and network pharmacology methods,and to provide ideas for the clinical use of Parkinson's disease and new drug development.Methods:The Chinese herbal medicine compound prescriptions for Parkinson's disease built up to May 31,2022 by searching the official website of patent publication notice.An Excel table was built,and after term normalization of the included compound prescriptions,Excel and IBM SPSS Modeler 18.0 were used for data mining such as frequency statistics.We also applied network pharmacology methods to study HF drugs,using TCMSP and TCM database@Taiwan to obtain drug components and using TCMSP platform and Swiss ADME screening,collecting targets through UniProt and Swiss Target Prediction platform;obtaining PD disease from databases such as GeneCards The targets were obtained from GeneCards and other databases,drug-disease target intersections were obtained,and"drug-disease-target"networks were created using Cytoscape 3.8 software,and GO and KEGG enrichment analyses were performed using the Metascapep platform.Results:A total of 113 patented Chinese medicine recipes were included,involving 394 drugs,among which Tianma,Angelica and Bai Shao were the most commonly used drugs.A total of 442 drug targets,4884 disease targets and 324 drug-disease common targets were obtained.Conclusion:The study found that the medicinal properties of the patented compound Chinese medicine for Parkinson's disease are mainly warm and cold,the taste of the medicine is mainly sweet and bitter,the normalizing meridian is mainly liver,heart and spleen meridians,and the treatment method is mainly to strengthen the liver and kidney,nourish the qi and blood,and extinguish the wind and dredge the luo.The core ingredients include 4-ethoxymethylphenyl-4'-hydroxyl,palmitic acid,lignan,etc.;the main action targets are PTGS2,PTGS1,SCN5A,etc.;involved in PI3K-Akt,cAMP and other signaling pathways.The development of relevant compound can be based on clinical symptoms,appropriate tailoring,and flexible use of such drugs,in order to obtain the best therapeutic effect.

Analysis on The Application of Data Mining Technology in Computer Network Virus Defense

With the rapid development of computer network,the society has entered the information and digital era,it plays an important role in speeding up the pace of social development and providing more convenient services for people.However, the security problem of computer network is becoming more and more serious. All kinds of network viruses pose a great threat to the security of computer network.As the most advanced data processing technology currently, data mining technology can effectively resist the invasion of network virus to computer system,and plays an important role in improving the security of the computer network.This paper starts with the concept of data mining technology and the characteristics of computer network virus,and makes an in-depth analysis on the specific application of data mining technology in the computer network virus defense.

Application of Data mining in Enterprise Network Marketing

With the rapid development of the Internet, market has been increasingly competitive and competition means are various. Internet Marketing has become a new way for enterprises to grow. Data mining of enterprise network marketing has become the new darling of many business managers.The marketing data will become a key to develop a corporate marketing strategy as an important basis tool. However, many companies now make mistakes in marketing data. They can＇t fully exploit the marketing data.lt can affect the development of enterprise network marketing strategy. This paper is based on the concept of marketing data and outline the importance of data mining for network marketing, then analyze a significant impact on the entemrise network marketin~ strate^w made by marketinR data mininR.

Research on the application of the data mining technology in the electronic commerce network marketing

With the rapid development of China＇s reform and opening up and the socialist market economy, the development of Internet technology has promoted the prosperity of e-commerce, and further promoted the rapid development of China＇s economy. Data mining technology is an advanced science and technology, which has important implications for the e-commerce data processing. Through the summary of the data mining technology, this article puts forward the application of data mining technology in electronic commerce, in order to better promote the development of electronic commerce.

Exploring the prescription in rules and mechanism for cerebral infarction based on data mining and Network Pharmacology

Objective: This study intends to explore the prescription rules for cerebral infarction treatment based on the famous prescription book Qian Jin Yao Fang, and further mined the core prescription and predicted the main active components and targets. Methods: The prescriptions for cerebral infarction in "Qian Jin Yao Fang" were collected. The data were analyzed using the auxiliary platform of traditional Chinese medicine V2.5, to explore the core herbs and summarized new prescriptions. At last, we analyzed the mechanism of core prescriptions of for cerebral infarction treatment using network pharmacology methods. Results: 100 prescriptions and 152 kinds of herbs were obtained by data mining. The results showed that deficiency-tonifying drugs account for a large proportion of drug classification;The core prescriptions for cerebral infarction in "Qian Jin Yao Fang" were composed of Herba Ephedrae(Mahaung), Cinnamomum cassia Presl(Rougui), Panax ginseng C.A.Meyer (Renshen), Glycyrrhiza uralensis Fisch(Gancao), Saposhnikovia divaricate(Trucz) Schischk(Fangfeng), Aconitum carmichaeli Debx(Fuzi) and Ligusticum chuanxiong Hort(Chuanxiong). Using network-based systems biology analysis, we predicted that 194 potential targets in the core prescriptions for cerebral infarction. 45 key targets of the core prescriptions were obtained in the treatment of cerebral infarction, suggesting that the related mechanism maybe closely related to TP53, STAT3, AKT1, MYC, FOS. Through Gene Oncology and Kyoto Encyclopedia of Genes and Genomes analysis, we found that the related signaling pathways mainly involved in PI3K-Akt signal pathway, TNF signal pathway, AGE-RAGE signal pathway, and so on. Conclusion: It takes expelling wind and relieving surface and warming spleen and kidney as the treatment method of cerebral infarction in "Qian Jin YaoFang". The core prescriptions were used for cerebral infarction treatment for the multi-component, multi-target and multi-channel interaction, such as PI3K-Akt signal pathway, TNF signal pathway, AGE-RAGE signal pathway and so on.

A retrospective study on professor Gui-Qi Xuan’s experience in traditional Chinese medicine treatment on attention-deficit hyperactivity disorder: based on data mining and network pharmacology

Background:To investigate the clinical medication approach of Professor Guiqi Xuan(Prof.Xuan)in treating pediatric patients with attention-deficit hyperactivity disorder(ADHD)and the potential mechanism of the core herbal prescription.Methods:Following medical record information pretreatment,the Traditional Chinese Medicine(TCM)inheritance computing platform system V3.0 was utilized to analyze the standardized data.The associate rules were summarized to identify the core prescription for treating ADHD.The extracted core herbal prescription’s active compounds and potential targets were used to establish a protein-protein interaction network of active ingredient-disease targets.Cytoscape 3.9.1 software was used to analyze the network’s topological parameters to obtain the key active ingredients and their targets.The Bioconductor data package of R4.0.2 was used to analyze the gene ontology biological functions and Kyoto Encyclopedia of Genes and Genomes pathways of key targets.Results:Two hundred and twenty-seven entries derived from TCM record information were selected.Through data mining,it was found that 62.5%of pediatric patients had short-tempered behavior,nearly half had sleep problems,and 30%-40%had picky eating and polyphagia issues.The highest-frequency syndrome type was kidney deficiency and liver hyperactivity.Deficiency,fire,phlegm,and dyspeptic food were the main pathological factors for ADHD.Prof.Xuan’s treatment of ADHD mainly focused on replenishing kidney essence and subduing Yang(active,external,ascending,warm,bright,functional and excited pertain to Yang).The core herbal prescription for ADHD included Yuan-zhi,Yi-zhi,Gui-jia,Bai-shao,Long-chi,Ci-shi,Shi-chang-pu,Yu-jin,Fu-shen,and Huang-jing.The protein-protein interaction network showed that MAOA,ADRB2,FOS,MAOB,and SLC6A3 were the five key targets essential in treating ADHD with core herbal prescriptions.The gene ontology biological function of crucial targets mainly involved G protein-coupled amine receptor activity,catecholamine binding,and neurotransmitter transmembrane transporter activity.Analysis of Kyoto Encyclopedia of Genes and Genomes pathways showed that the dopaminergic synapse signaling and neuroactive ligand-receptor interaction pathways were significantly enriched and may be the primary routes for the main treatment of ADHD.Conclusion:Prof.Xuan’s treatment of ADHD has achieved satisfactory clinical effects by supplementing the kidney,replenishing the essence,opening the orifices,nourishing the Yin(static,internal,descending,cold,dim,organic,depressed and pertain to Yin),and subduing the Yang.The major prescription predominantly affects catecholamine binding,neuroactive ligand-receptor interaction,G protein-coupled amine receptor function,and signaling pathways for dopaminergic synapses.Our findings showed that the methodology and software used in this research could explore and analyze the mechanism behind Prof.Xuan’s clinical medication rule for treating ADHD in children.

Analysis of drug use law and mechanism of prostate cancer based on data mining and network pharmacology

Objective: Excavate the medication rule of traditional Chinese medicine in the treatment of prostate cancer, and predicting the biomolecular level mechanism of high-frequency drug compatibility. Methods: Relevant documents in CNKI, Wanfang Medical Network and VIP Chinese Biomedical Periodical Database Pubmed, EMbase were collected and collated systematically. Frequency statistics, association rule analysis and new party mining were carried out using TCMISSV2.5. BATMAN-TCM was used to analyze the interaction relationship and related pathways between high-frequency drug targets. Results: Huangqi (Astragalus membranaceus) was the single drug most used of the 102prescriptions included in the standard. There are 6 pairs of combinations with high confidence in association rule analysis. System entropy cluster analysis resulted in 20 core drug combinations and 9 new prescriptions. Through KEGG pathway analysis of Huangqi, Fuling (Poria cocos), Gancao (Glycyrrhiza uralensis) and Dihuang (Rehmannia glutinosa), it was found that the number of potential targets of the neural active ligand receptor rented pathway and purine metabolism pathway was the largest. Conclusions: Prostate cancer is mainly treated with deficiency-tonifying drugs, which are combined with drugs for promoting blood circulation, removing blood stasis, clearing heat, promoting diuresis, detoxifying and resolving hard mass. The mechanism of action of high-frequency traditional Chinese medicine may be realized by interfering with the neuroactive ligand receptor interaction pathway and purine metabolism pathway.

Identifying medication regularity of traditional Chinese medicine and potential pharmacological mechanism of Jiedu Sangen decoction in colorectal cancer treatment by data mining and network pharmacology

Objective:Colorectal cancer(CRC)is one of the most common malignancies in the world,and traditional Chinese medicine(TCM)is widely used in its treatment in China.However,the medication rules of TCM for CRC treatment remain unclear.Therefore,data mining combined with network pharmacology was utilized to establish treatment principles and rules.Methods:The CRC cases treated at Zhejiang Provincial Hospital of Chinese Medicine from January 1,2016 to October 31,2021 were analyzed using data mining methods.UPLC-Q/TOF-MS analysis was performed to identify the chemical composition of Jiedu Sangen decoction(JSD).Network pharmacology was used to reveal the therapeutic mechanism of JSD.Results:A total of 312 cases 2,998 prescriptions that met the inclusion criteria used 343 kinds of traditional Chinese medicines.The nature of the herbs used in treatment was mainly warm and mild.The taste was mainly sweet,bitter,and pungent.The meridian tropisms were mainly the spleen meridian,followed by lung and stomach meridians.Tonifying and replenishing herbs were the most frequently used in treatment.High-frequency herbs were classified into 11 categories by cluster analysis,and 42 association rules were obtained by association rule analysis.Combined with complex network analysis,3 core prescriptions for clinical CRC treatment were obtained.Jiedu Sangen decoction contains 64 chemical ingredients,out of which 31 active ingredients were identified,including polydatin,caffeic acid,and glutamic acid,along with 130 potential targets such as AKT1,SRC,and MAPK1.Jiedu Sangen decoction may play a role in regulating inflammation,immunity,metabolism,and hormones in the development of CRC via pathways such as the relaxin signaling pathway,IL-17 signaling pathway,prolactin signaling pathway,and T cell receptor signaling pathway.Conclusions:This study summarizes the treatment and medication principles for clinical CRC treatment,promoting the inheritance and development of the traditional Chinese medical experience.