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

Abstract

Background: 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.

Methods: 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 effectors and effects.

Results: 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 http://tcm.lifescience.ntu.edu.tw/.

Conclusion: 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.

Figure 1

The simplified relational scheme of TCMGeneDIT. Each gray box represents an entity with various major attributes characterized by oval-shape. For instance, each TCM herb may be associated with one or many genes involving in several signaling pathways and have many interacting partners. Theses associations may be related to the therapeutic mechanisms for certain diseases and could be supported by scientific evidences.

Figure 2

The text mining approach and information integration for TCMGeneDIT. Literature corpus about TCMs was collected from PubMed and used for entity annotations and information extraction. Annotated documents were mined based on hypothesis test and collocation analysis to discover entity associations. On the other hand, the raw corpus was pre-processed with public tools and several rules can be applied to the processed sentences for extracting the relations between effecters and effects. The constructed effect set was used to filter candidate relation and literature annotation. Protein-protein interactions and biological pathways were integrated into our database and disease candidate genes from PharmGKB were used for transitive inference. Users can access the database via the web interface. Thick arrow indicates the basic work flow of TCMGeneDIT.

Figure 3

The TCM and gene associations and visual information representations. (a) The Ganoderma lucidum and gene associations from text mining. Detailed information about TCM and genes could be accessed by following the links themselves. Literature evidences supporting the associations are available through following the links indicating the number of paper. Confidence thresholds are able to be selected. Users with domain knowledge can recommend the associations they think are correct. Moreover, TCM and gene associations could be discovered by transitive inference. (b) TCM and gene association graph. TCM and genes are represented by green and yellow nodes, respectively. The edges between them are colored according to t values (please see the text). The numbers on the edges mean how many literatures may support the associations. (c) TCM, gene and disease association graph. Red nodes mean diseases.