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text mining of Rheumatoid Arthritis and Diabetes Mellitus to Understand the Mechanisms of Chinese Medicine in Different Diseases with Same Treatment

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Chinese Journal of Integrative Medicine 2018年第10期24卷 777-784页

作者： ZHAO Ning ZHENG Guang LI Jian ZHAO Hong-yan LU Cheng JIANG Miao ZHANG Chi GUO Hong-tao LU Ai-ping Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical Sciences School of Information Science and Engineering Lanzhou University School of Basic Medical Sciences Beijing University of Chinese Medicine Institute of Basic Theory for Chinese Medicine China Academy of Chinese Medical Sciences School of Chinese Medicine Hong Kong Baptist University

Objective： To identify the commonalities between rheumatoid arthritis （RA） and diabetes mellitus （DM） to understand the mechanisms of Chinese medicine （CM） in different diseases with the same treatment. Methods： A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identified via text mining was used as the intervention. Results： Similarities were identified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM. Conclusion： Commonalities between the biological activities involved in RA and DM were identified through text mining, and both RA and DM might be responsive to the same intervention at a specific stage.

关键词： text mining rheumatoid arthritis diabetes mellitus different diseases with same treatment Chinese medicine

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Deep Analysis of Power Equipment Defects Based on Semantic Framework text mining Technology

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1157-1164页

作者： Huifang Wang Jing Cao Dongyang Lin Electrical Engineering Department Zhejiang UniversityHangzhou 310058China State Grid Jiangsu Electric Power Engineering Consulting Co. Ltd.Nanjing 210000China

Defect factors and their relevant rules can be analyzed in depth by processing defect records which are often expressed in the form of text data.However,considering that defect text consists of both structured and unstructured data,it is necessary to excavate structured information from unstructured data.In this paper,a text mining method based on semantic framework technology is introduced to transform unstructured defect description into structured information such as components and defect attributes.Then,a deep analyzing model of a power equipment defect is established,which provides a scheme of defect mining based on historical defect texts.Case studies prove that the proposed deep analysis method has a guiding significance for equipment upgrading,selection and maintenance.

关键词： Age curve defect analysis defect rate factor study power equipment text mining

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text mining and Analysis of Treatise on Febrile Diseases Based on Natural Language Processing

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World Journal of Traditional Chinese Medicine 2020年第1期6卷 67-73页

作者： Kai Zhao Na Shi Zhen Sa Hua-Xing Wang Chun-Hua Lu Xiao-Ying Xu School of Traditional Chinese Medicine Beijing University of Chinese Medicine School of Life Science Beijing University of Chinese Medicine Beijing 100029China

Objective:With using natural language processing (NLP) technology to analyze and process the text of "Treatise on Febrile Diseases (TFDs)"for the sake of finding important information, this paper attempts to apply NLP in the field of text mining of traditional Chinese medicine (TCM)literature. Materials and Methods:Based on the Python language, the experiment invoked the NLP toolkit such as Jieba, nltk, gensim,and sklearn library, and combined with Excel and Word software. The text of "TFDs" was sequentially cleaned, segmented, and moved the stopped words, and then implementing word frequency statistics and analysis, keyword extraction, named entity recognition (NER) and other operations, finally calculating text similarity. Results:Jieba can accurately identify the herbal name in "TFDs." Word frequency statistics based on the word segmentation found that "warm therapy" is an important treatment of "TFDs." Guizhi decoction is the main prescription,and five core decoctions are identified. Keyword extraction based on the term "frequency-inverse document frequency" algorithm is ideal.The accuracy of NER in "TFDs" is about 86%;latent semantic indexing model calculating the similarity,"Understanding of Synopsis of Golden Chamber (SGC)" is much more similar with "SGC" than with "TFDs." The results meet expectation. Conclusions:It lays a research foundation for applying NLP to the field of text mining of unstructured TCM literature. With the combination of deep learning technology,NLP as an important branch of artificial intelligence will have broader application prospective in the field of text mining in TCM literature and construction of TCM knowledge graph as well as TCM knowledge services.

关键词： Knowledge discovery natural language processing text mining traditional Chinese medicine literature treatise on febrile diseases

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Identifying miRNA biomarkers of polycystic ovary syndrome through text mining

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Reproductive and Developmental Medicine 2023年第2期7卷 96-101页

作者： Yan Wang Xiao-Jing Yuan Bin Zhao Medical Reproductive Center People’s Hospital of Jiuquan CityJiuquan 735099GansuChina Xiamen Health and Medical Big Data Center Xiamen 361008FujianChina

Objective:Polycystic ovary syndrome(PCOS)is an endocrine disorder with diverse clinical manifestations that often occurs in women of childbearing age.However,its molecular pathogenesis remains unclear,and this study aimed to identify miRNA targets in PCOS through text mining and database analysis.Methods:First,three different sets of text mining genes(TMGs)associated with"polycystic ovary syndrome","obesity/adiposis",and"anovulation"keywords were retrieved from the GenCLiP3 database,and overlapping genes were selected.Second,Gene ontology annotation and biological pathway enrichment analyses of these overlapping TMGs were performed,followed by protein-protein interaction(PPI)network analysis.Third,genes in the gene module clustered in the PPI were selected to predict potential miRNAs for PCOS via miRNA-mRNA analysis.Results:A total of 4291 TMGs related to three different keywords were obtained through text mining;72 intersect TMGs were retained among the three gene sets,and 62 TMGs participated in the establishment of the PPI network,of which 18 were aggregated in the gene module.Finally,11 miRNAs that simultaneously bound to two TMGs(IGF1,ESR1,MAPK1,NAMPT,PIK3CA,and SERPINE1)could be prioritized as targets to study PCOS.Conclusion(s):The discovery of 11 miRNAs(miR-301a-3p,miR-301b-3p,miR-3666,miR-454-3p,miR-130a-3p,miR-130b-3p,miR-4295,miR-190a-3p,miR-5011-5p,miR-548c-3p,and miR-4799-5p)and 6 TMGs,which are associated with the HIF-1 signaling pathway(P=4.799E-08),could be used as potential targets for PCOS.

关键词： text mining Polycystic ovary syndrome Obesity Anovulation microRNA

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Drug discovery and potential gene and pathway associated with polycystic ovary syndrome through text mining and biomedical databases

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Reproductive and Developmental Medicine 2023年第1期7卷 44-49页

作者： Yan Wang Bin Zhao Xiao-Jing Yuan Medical Reproductive Center People’s Hospital of Jiuquan CityJiuquan 735099GansuChina Xiamen Health and Medical Big Data Center Xiamen 361008FujianChina

Objectives:Polycystic ovary syndrome(PCOS)is a common endocrine disease in women of childbearing age.Although it is a leading cause of menstrual disorders,infertility,obesity,and other diseases,its molecular mechanism remains unclear.This study aimed to analyze the target genes,pathways,and potential drugs for PCOS through text mining.Methods:First,three different keywords("polycystic ovary syndrome","obesity/adiposis",and"anovulation")were uploaded to GenCLiP3 to obtain three different gene sets.We then chose the common genes among these gene sets.Second,we performed gene ontology and signal pathway enrichment analyses of these common genes,followed by protein-protein interaction(PPI)network analysis.Third,the most significant gene module clustered in the protein-protein network was selected to identify potential drugs for PCOS via gene-drug analysis.Results:A total of 4291 genes related to three different keywords were obtained through text mining,72 common genes were filtered among the three gene sets,and 69 genes participated in PPI network construction,of which 23 genes were clustered in the gene modules.Finally,six of the 23 genes were targeted by 30 existing drugs.Conclusions:The discovery of the six genes(CYP19A1,ESR1,IGF1R,PGR,PTGS2,and VEGFA)and 30 targeted drugs,which are associated with ovarian steroidogenesis(P<0.001),may be used in potential therapeutic strategies for PCOS.

关键词： text mining Bioinformatics Polycystic ovary syndrome Obesity Anovulation

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text mining and data analysis identify potential drugs and pathways for polycystic ovary syndrome treatment

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生殖与发育医学(英文) 2024年第1期08卷 32-38页

作者： Yuan Xiao-Jing Wang Yan Yang Hai-Yan Zhao Bin Medical Reproductive Center People’s Hospital of Jiuquan City 3. Jiuquan 735000 4. China Department of Gynecological Oncology The Fourth Affiliated Hospital of Guangxi Medical University 7. Liuzhou 545000 8. Xiamen Health and Medical Big Data Center 9. Xiamen 361001

Objective: Polycystic ovarian syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. This study aimed to use text mining and microarray data analysis to identify drugs that target genes and potential pathways associated with PCOS.Methods: We extracted a common set of genes associated with PCOS using text mining and the microarray dataset GSE48301. Next, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of these genes, as well as protein–protein interaction (PPI) network analysis. Additionally, we used MCODE and cytoHubba to cluster significant common genes in the PPI network and performed gene–drug interaction analyses to identify potential drugs for further investigation. Finally, we annotated pathways associated with the genes identified.Results: text mining and microarray analysis yielded 696 text mining genes (TMGs) and 2804 differentially expressed genes (DEGs). Among these, a set of 77 genes was found in both TMGs and DEGs. Interestingly, 67 of these genes participated in constructing the PPI network. Seven common hub genes were selected using the MCODE and CytoHubba methods. Finally, five out of seven genes were targeted by 15 existing drugs.Conclusion: Four genes (FASLG, IL13, IL17A, andIL2RA), which are mainly related to the cytokine–cytokine receptor interaction pathway, could be prioritized as targets for PCOS.

关键词： text mining Differentially expressed genes Polycystic ovary syndrome Drug discovery

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Blockchain technology forecasting by patent analytics and text mining

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Blockchain(Research and Applications) 2021年第2期2卷 97-110页

作者： Seyed Mojtaba Hosseini Bamakan Alireza Babaei Bondarti Parinaz Babaei Bondarti Qiang Qu Department of Industrial Management Yazd UniversityYazd89195-741Iran Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Department of Library and Information Science Kharazmi UniversityTehran15719-14911Iran

Information technologies(ITs)have been playing an important role in improving our society,and the fast evolution of ITs creates a competitive environment not only for companies but also for regions.Hence,recognizing the future trend of technologies can be effective in decision-making with regard to technology selection and investment.Blockchain technology with its vast and impressive applications has received considerable attention from researchers,investors,and public agencies.The purpose of this research is to investigate blockchain technology to explore its trends according to their classification by use of the World Intellectual Property Organization(WIPO)database.Furthermore,we particularly evaluate the registered patents in the world's most well-known patent databases such as the USA patent database.We drew the current technology trends in blockchain patents by applying the text mining and clustering approach.The results represent that the registered patents in the USA patent database have been achieved in the growth phase.That means,attention to the blockchain is rising nowadays and most patents focused the cryptocurrencies and their applications in finance.However,blockchain technology is in the emergence phase and is evolving by researchers and inventors.

关键词： Technology forecasting Blockchain technology Patent analysis text mining Technology life cycle

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Automatic Surveillance of Pandemics Using Big Data and text mining

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Computers, Materials & Continua 2021年第7期68卷 303-317页

作者： Abdullah Alharbi Wael Alosaimi MIrfan Uddin Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan

COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms.

关键词： Disease surveillance social media analysis recurrent neural networks text mining

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text-mining analysis using national R&D project data of South Korea to investigate innovation in graphene environment technology

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International Journal of Innovation Studies 2023年第1期7卷 87-99页

作者： Ji Yeon Lee Jung Woo Choi Jae-Hak Choi Byeong-Hee Lee NTIS Center Korea Institute of Science and Technology InformationDaejeon34141South Korea Department of Science and Technology Management Policy University of Science and TechnologyDaejeon34113South Korea Department of Polymer Science and Engineering Chungnam National UniversityDaejeon34134South Korea

This study investigates the possibilities offered by the development of graphene environment technology that can contribute to new and effective approaches for the transition to an ecologically benign ecosystem.To analyze the research and development progress in graphene environment technology,graphene environment technology research trends of South Korea for the years 2009e2020 are investigated by acquiring information pertaining to national research and development projects related to graphene environment technology from the National Science and Technology Information Service.Both structured and unstructured data are analyzed using diverse text-mining methods,such as keyword frequency analysis,association rule mining,and topic modelling.The results indicate that graphene research in South Korea is focused primarily on graphene use in batteries and energy-storage devices,such as solar cells,fuel cells,and secondary batteries.This study can help understand the manner by which the South Korean government has been investing in the research and development of graphene environment technology;additionally,it discusses the future applications and prospects of graphene for the next decade.

关键词： Graphene technology Environmental transition National R&D project information text mining Trend analysis

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New Challenges for Biological text-mining in the Next Decade

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Journal of Computer Science & Technology 2010年第1期25卷 169-178,F0003页

作者： Yen-Ching Chang Richard Tzong-Han Tsai Wen-Lian Hsu Institute of Information Science "Academia Sinica" Department of Computer Science and Engineering Yuan Ze University Department of Computer Science "National Tsing-Hua University"

The massive flow of scholarly publications from traditional paper journals to online outlets has benefited biologists because of its ease to access. However, due to the sheer volume of available biological literature, researchers are finding it increasingly difficult to locate needed information. As a result, recent biology contests, notably JNLPBA and BioCreAtIvE, have focused on evaluating various methods in which the literature may be navigated. Among these methods, text-mining technology has shown the most promise. With recent advances in text-mining technology and the fact that publishers are now making the full texts of articles available in XML format, TMSs can be adapted to accelerate literature curation, maintain the integrity of information, and ensure proper linkage of data to other resources. Even so, several new challenges have emerged in relation to full text analysis, life-science terminology, complex relation extraction, and information fusion. These challenges must be overcome in order for text-mining to be more effective. In this paper, we identify the challenges, discuss how they might be overcome, and consider the resources that may be helpful in achieving that goal.

关键词： bioinformatics database mining method and algorithm text mining

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