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

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

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Modelo para descoberta de conhecimento baseado em associação semântica e temporal entre elementos textuais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2016.O aumento da complexidade nas atividades organizacionais, a vertiginosa expansão da Internet e os avanços da sociedade do conhecimento são alguns dos responsáveis pelo volume inédito de dados digitais. Essa crescente massa de dados apresenta grande potencial para a análise de padrões e descoberta de conhecimento. Nesse sentido, a análise dos relacionamentos presentes nesse imenso volume de informações pode proporcionar novos e, possivelmente, inesperados insights. A presente pesquisa constatou a escassez de trabalhos que consideram adequadamente a semântica e a temporalidade dos relacionamentos entre elementos textuais, características consideradas importantes para a descoberta de conhecimento. Assim, este trabalho propõe um modelo para descoberta de conhecimento que conta com uma ontologia de alto-nível para a representação de relacionamentos e com a técnica Latent Semantic Indexing (LSI) para determinar a força de associação entre termos que não se relacionam diretamente. A representação do conhecimento de domínio, bem como, a determinação da força associativa entre os termos são realizadas levando em conta o tempo em que os relacionamentos ocorrem. A avaliação do modelo foi realizada a partir de dois tipos de experimentos: um que trata da classificação de documentos e outro que trata da associação semântica e temporal entre termos. Os resultados demonstram que o modelo: i) possui potencial para ser aplicado em tarefas intensivas em conhecimento, como a classificação e ii) é capaz de apresentar curvas da força associativa entre dois termos ao longo do tempo, contribuindo para o levantamento de hipóteses e, consequentemente, para a descoberta de conhecimento.Abstract : The increased complexity in organizational activities, the rapid expansion of the Internet and advances in the knowledge society are some of those responsible for the unprecedented volume of digital data. This growing body of data has great potential for pattern analysis and knowledge discovery. In this sense, the analysis of relationships present in this immense volume of information can provide new and possibly unexpected insights. This research found shortages of studies that adequately consider the semantics and the temporality of relationships between textual elements considered important features for knowledge discovery. This work proposes a model of knowledge discovery comprising a high-level ontology for the representation of relationships and the LSI technique to determine the strength of association between terms that do not relate directly. The representation of domain knowledge and the determination of the associative strength between the terms are made taking into account the time in which the relationships occur. The evaluation of the model was made from two types of experiments: one that deals with the classification of documents and another concerning semantics and temporal association between terms. The results show that the model: i) has the potential to be used as a text classifier and ii) is capable of displaying curves of associative force between two terms over time, contributing to the raising of hypotheses and therefore to discover of knowledge