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

doi:10.1093/nar/gkh484

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 journal › Article › peer-review

32 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	W230-W234
Journal	NUCLEIC ACIDS RESEARCH
Volume	32
Issue number	WEB SERVER ISS.
DOIs	https://doi.org/10.1093/nar/gkh484
State	Published - Jul 1 2004
Externally published	Yes

ASJC Scopus subject areas

Genetics

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@article{53c25386df0c45fb850bbc867ae1ed21,

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

abstract = "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/.",

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

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AU - Zuo, Li

AU - Choudhary, Vidhu

AU - Zhang, Zhuo

AU - Leow, Shoi Houi

AU - Chong, Fui Teen

AU - Huang, Yingliang

AU - Siong Ong, Victor Wui

AU - Mohanty, Bijayalaxmi

AU - Tan, Sin Lam

AU - Krishnan, S. P.T.

AU - Bajic, Vladimir B.

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N2 - 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/.

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

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