Dragon TF Association Miner: a system for exploring transcription factor associations through text-mining | Zendy

Hongyu Pan | Zendy; Lin Zuo | Zendy; Vijayata Choudhary | Zendy; Zhihui Zhang | Zendy; S. H. Leow | Zendy; Allen Chong | Zendy; Yulong Huang | Zendy; V. W. S. Ong | Zendy; Bijayalaxmi Mohanty | Zendy; Sin Lam Tan | Zendy; Sivanand Krishnan | Zendy; Vladimir B. Bajić | Zendy

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

Author(s) -

Hongyu Pan,

Lin Zuo,

Vijayata Choudhary,

Zhihui Zhang,

S. H. Leow,

Allen Chong,

Yulong Huang,

V. W. S. Ong,

Bijayalaxmi Mohanty,

Sin Lam Tan,

Sivanand Krishnan,

Vladimir B. Bajić

Publication year - 2004

Publication title -

nucleic acids research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 9.008

H-Index - 537

eISSN - 1362-4954

pISSN - 0305-1048

DOI - 10.1093/nar/gkh484

Subject(s) - decipher , biology , gene ontology , computational biology , association rule learning , information retrieval , annotation , transcription factor , gene , data mining , computer science , bioinformatics , genetics , gene expression

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/.

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