Dragon TF Association Miner: A system for exploring transcription factor associations through text-mining

Hong Pan, Li Zuo, Vidhu Choudhary, Zhuo Zhang, Shoi Houi Leow, Fui Teen Chong, Yingliang Huang, Victor Wui Siong Ong, Bijayalaxmi Mohanty, Sin Lam Tan, S. P.T. Krishnan, Vladimir B. Bajic*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


We present Dragon TF Association Miner (DTFAM), a system for text-mining of PubMed documents for potential functional association of transcription factors (TFs) with terms from Gene Ontology (GO) and with diseases. DTFAM has been trained and tested in the selection of relevant documents on a manually curated dataset containing >3000 PubMed abstracts relevant to transcription control. On our test data the system achieves sensitivity of 80% with specificity of 82%. DTFAM provides comprehensive tabular and graphical reports linking terms to relevant sets of documents. These documents are color-coded for easier inspection. DTFAM complements the existing biological resources by collecting, assessing, extracting and presenting associations that can reveal some of the not so easily observable connections among the entities found which could explain the functions of TFs and help decipher parts of gene transcriptional regulatory networks. DTFAM summarizes information from a large volume of documents saving time and making analysis simpler for individual users. DTFAM is freely available for academic and non-profit users at http://research.i2r.a-star.edu.sg/DRAGON/TFAM/.

Original languageEnglish (US)
Pages (from-to)W230-W234
Issue numberWEB SERVER ISS.
StatePublished - Jul 1 2004
Externally publishedYes

ASJC Scopus subject areas

  • Genetics


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