Database

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

Yu-Ching Fang¹ , Hsuan-Cheng Huang² , Hsin-Hsi Chen³ and Hsueh-Fen Juan^1,4,5,6

¹  Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan

²  Institute of Biomedical informatics & Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan

³  Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

⁴  Department of Life Science, National Taiwan University, Taipei, Taiwan

⁵  Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

⁶  Center for Systems Biology and Bioinformatics, National Taiwan University, Taipei, Taiwan

author email corresponding author email

BMC Complementary and Alternative Medicine 2008, 8:58doi:10.1186/1472-6882-8-58

Published:	14 October 2008

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 effecters 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/ webcite.

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.