Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge

AbstractObjectiveTo explore multiple relationships in traditional Chinese medicine (TCM) knowledge by comparing binary and multiple relationships during knowledge organization.MethodsCharacteristics of binary and multiple semantic relationships as well as their associations are described. A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces. The classification includes parallel multiple relationships, restricted multiple relationships, multiple relationships that involve time, and multiple relationships that involve time restriction. Next, construction of multiple semantic relationships for TCM concepts in each classification using Protégé, an ontology editing tool is described.ResultsProtégé is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.ConclusionWhen applied in TCM, the semantic relationships constructed by Protégé are superior than those constructed by correlation and/or attribute relationships, but less ideal than those constructed by the human cognitive process

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Ontologies and Computational Methods for Traditional Chinese Medicine

Perinteinen kiinalainen lääketiede (PKL) on tuhansia vuosia vanha hoitomuoto, jonka tarkoituksena on terveyden ylläpito, tautien ennaltaehkäisemisen ja terveydellisten ongelmien hoito. Useat vuosittain julkaistavat tutkimukset tukevat hoitojen tehokkuutta ja PKL onkin jatkuvasti kasvattamassa suosiotaan maailmanlaajuisesti. Kiinassa PKL ollut suosittu hoitomuoto jo pitkään ja nykyään sitä harjoitetaan rinnakkain länsimaisen lääketieteen kanssa. Viime vuosikymmeninä tapahtuneen tietotekniikan kehityksen ja yleistymisen myötä myös PKL:n menetelmät ovat muuttuneet ja tietotekniikkaa on alettu hyödyntämään PKL:n tutkimuksessa. PKL:n tietoa on tallennettu digitaaliseen muotoon, minkä seurauksena on syntynyt suuri määrä erilaisia tietokantoja. Tieto on jakautunut eri tietokantoihin, joiden terminologia ei ole yhtenevää. Tämä aiheuttaa ongelmia tiedon löytämisessä ja tietoa hyödyntävien sovellusten kehittämisessä. Tässä työssä selvitetään, mitä PKL on, ja mikä sen asema on nykyään Kiinassa ja muualla maailmalla. Työn tarkoituksena on tutkia PKL:n tietoteknisten sovelluksen kehittämistä ja siihen liittyviä haasteita. Työssä perehdytään PKL:n ontologioiden ja semanttisten työkalujen toimintaan, sekä PKL:n laskennallisiin menetelmiin ja niiden tarjoamiin mahdollisuuksiin. Lisäksi kerrotaan uusimmista kansainvälisesti merkittävistä projekteista ja pohditaan tulevaisuuden näkymiä. Jo kehitetyt PKL:n tietotekniset sovellukset tarjoavat uusia mahdollisuuksia tiedon etsimiseen ja parantavat tutkijoiden mahdollisuutta jakaa tietoa ja tehdä yhteistyötä. Tietokoneavusteiset diagnoosityökalut ja asiantuntijajärjestelmät tarjoavat mahdollisuuksia lääkärin tekemän diagnoosin varmistamiseen. Tulevaisuudessa laskennallisia menetelmiä hyödyntäen voitaisiin tarjota terveyttä ja hyvinvointia edistäviä palveluja verkossa.Traditional Chinese Medicine (TCM) has been used for thousands of years in China for the purposes of health maintenance, disease prevention and treatment of health problems. Several published studies support the effectiveness of TCM treatments and the global use of TCM is constantly increasing. In China, Western and Chinese medicine are practiced in parallel. During the past few decades, the use of information technology in medicine has increased rapidly. The development of information technology has opened up new possibilities for information storage and sharing, as well as communication and interaction between people. Along with the growing use of information technology, a wide variety of patient databases and other electronic sources of information have emerged. However, the information is fragmented and dispersed, and the terminology is ambiguous. The objective of the thesis is to examine the position of TCM today, and to find out what changes and new opportunities the modern information technology brings for different aspects of TCM. This study describes how ontologies and semantic tools can be utilized when collecting existing knowledge and combining different databases. Also different computational methods and TCM expert systems are introduced. Finally, the most recent projects in the field of TCM are discussed and the future challenges are reflected. The computational methods for TCM, such as diagnostic tools and expert systems, could be very useful in anticipating and preventing health problems. E-science and knowledge discovery offer new ways for knowledge sharing and cooperation. TCM expert systems can be used to generate diagnosis or automatic clinical alerts. In the future, a comprehensive and easily accessible online health service system could be developed and used to improve the health and well-being of people

TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining

<p>Abstract</p> <p>Background</p> <p>Traditional Chinese Medicine (TCM), a complementary and alternative medical system in Western countries, has been used to treat various diseases over thousands of years in East Asian countries. In recent years, many herbal medicines were found to exhibit a variety of effects through regulating a wide range of gene expressions or protein activities. As available TCM data continue to accumulate rapidly, an urgent need for exploring these resources systematically is imperative, so as to effectively utilize the large volume of literature.</p> <p>Methods</p> <p>TCM, gene, disease, biological pathway and protein-protein interaction information were collected from public databases. For association discovery, the TCM names, gene names, disease names, TCM ingredients and effects were used to annotate the literature corpus obtained from PubMed. The concept to mine entity associations was based on hypothesis testing and collocation analysis. The annotated corpus was processed with natural language processing tools and rule-based approaches were applied to the sentences for extracting the relations between TCM effecters and effects.</p> <p>Results</p> <p>We developed a database, TCMGeneDIT, to provide association information about TCMs, genes, diseases, TCM effects and TCM ingredients mined from vast amount of biomedical literature. Integrated protein-protein interaction and biological pathways information are also available for exploring the regulations of genes associated with TCM curative effects. In addition, the transitive relationships among genes, TCMs and diseases could be inferred through the shared intermediates. Furthermore, TCMGeneDIT is useful in understanding the possible therapeutic mechanisms of TCMs via gene regulations and deducing synergistic or antagonistic contributions of the prescription components to the overall therapeutic effects. The database is now available at <url>http://tcm.lifescience.ntu.edu.tw/</url>.</p> <p>Conclusion</p> <p>TCMGeneDIT is a unique database that offers diverse association information on TCMs. This database integrates TCMs with biomedical studies that would facilitate clinical research and elucidate the possible therapeutic mechanisms of TCMs and gene regulations.</p

Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the properties, meridians. Similarly, ingredients from the same cluster are more similar in structure and protein target. In summary, this thesis intends to build a bridge between the TCM system and modern medicinal systems using computational tools, including the machine learning model for meridian theory, network modelling for TCM formulae, as well as multipartite network analysis for herbal medicines and their ingredients. We demonstrated that applying novel computational approaches on the integrated high-throughput omics would provide insights for TCM and accelerate the novel drug discovery as well as repurposing from TCM.Perinteinen kiinalainen lääketiede (TCM) on ilmeinen tehokkuus taudin hoidoissa ja on arvokas lähde uuden lääkkeen löytämiseen. TCM: n farmakologisten vaikutusten taustalla oleva mekanismi pysyy kuitenkin tuntemattomassa, koska TCM on monimutkainen järjestelmä, jossa on useita yrttejä ja ainesosia, jotka tulevat yhteen reseptilääkkeeksi. Siksi on kiireellistä soveltaa Laskennallisia työkaluja TCM: lle ymmärtämään TCM-teorioiden taustalla oleva mekanismi molekyylitasolla ja käyttävät kehittyneitä verkkoalgoritmeja tutkimaan mahdollisia tehokkaita ainesosia ja havainnollistavat TCM: n periaatteita järjestelmän biologisissa näkökohdissa. Tässä opinnäytetyössä pyrimme ymmärtämään monimutkaisten TCM-järjestelmien toimintamekanismia molekyylitasolla bioinformaattilla ja laskennallisilla työkaluilla. Tutkimuksessa kehitettiin koneen oppimiskehystä yrttien ja ainesosien meridialaisista. Lopuksi saavutimme korkean tarkkuuden meridiaaneista yrtteistä ja ainesosista, mikä viittaa meridiaaneihin ja ainesosien ja yrtteihin liittyvien molekyylipiirin välillä, erityisesti koneen oppimismalleihin tärkeimmät ominaisuudet. Toiseksi ehdoimme uuden verkon lähestymistavan TCM-kaavojen tutkimiseksi kvantitoimisella vuorovaikutteisten yrttiparien vuorovaikutuksen tutkimuksessa ⅱ käyttämällä viisi verkkoetäisyyttä, mukaan lukien lähin, lyhyt, keskus, ydin sekä erottaminen. Osoitimme, että ylä-yrttiparien etäisyys on lyhyempi kuin satunnaisten yrttiparien, mikä viittaa voimakkaaseen vuorovaikutukseen ihmisellä vuorovaikutteisesti. Lisäksi Center-menetelmät ainesosan tasolla ylittivät muut menetelmät. Se vihjeitä meille, että keskeiset ainesosat ovat tärkeässä asemassa yrtteissä. Kolmanneksi tutkimme yrttien tai ainesosien välisiä yhdistyksiä ja niiden tärkeitä biologisia ominaisuuksia tutkimuksessa III, kuten ominaisuudet, meridiaanit, rakenteet tai tavoitteet klustereiden kautta moniparite-verkoston yhteisön analyysistä. Löysimme, että kasviperäiset lääkkeet samoilla klusterien keskuudessa ovat yleensä samankaltaisia ominaisuuksissa, meridiaaneissa. Samoin saman klusterin ainesosat ovat samankaltaisempia rakenteissa ja proteiinin tavoitteessa. Yhteenvetona tämä opinnäytetyö aikoo rakentaa silta TCM-järjestelmän ja nykyaikaisten lääkevalmisteiden välillä laskentatyökaluilla, mukaan lukien Meridian-teorian koneen oppimismalli, TCM-kaavojen verkkomallinnus sekä kasviperäiset lääkkeet ja niiden ainesosat Osoitimme, että uusien laskennallisten lähestymistapojen soveltaminen integroidulle korkean suorituskyvyttömiehille tarjosivat TCM: n näkemyksiä ja nopeuttaisivat romaanin huumeiden löytöä sekä toistuvat TCM: stä

Elucidation of the mechanisms and molecular targets of Yiqi Shexue formula for treatment of primary immune thrombocytopenia based on network pharmacology

Yiqi Shexue formula (YQSX) is traditionally used to treat primary immune thrombocytopenia (ITP) in clinical practice of traditional Chinese medicine. However, its mechanisms of action and molecular targets for treatment of ITP are not clear. The active compounds of YQSX were collected and their targets were identified. ITP-related targets were obtained by analyzing the differential expressed genes between ITP patients and healthy individuals. Protein-protein interaction (PPI) data were then obtained and PPI networks of YQSX putative targets and ITP-related targets were visualized and merged to identify the candidate targets for YQSX against ITP. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out. The gene-pathway network was constructed to screen the key target genes. In total, 177 active compounds and 251 targets of YQSX were identified. Two hundred and thirty differential expressed genes with an P value 1 were identified between ITP patient and control groups. One hundred and eighty-three target genes associated with ITP were finally identified. The functional annotations of target genes were found to be related to transcription, cytosol, protein binding, and so on. Twenty-four pathways including cell cycle, estrogen signaling pathway, and MAPK signaling pathway were significantly enriched. MDM2 was the core gene and other several genes including TP53, MAPK1, CDKN1A, MYC, and DDX5 were the key gens in the gene-pathway network of YQSX for treatment of ITP. The results indicated that YQSX's effects against ITP may relate to regulation of immunological function through the specific biological processes and the related pathways. This study demonstrates the application of network pharmacology in evaluating mechanisms of action and molecular targets of complex herbal formulations