Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/27709
Title: Recognition of transcription factor interactions based on NLP and artificial intelligence
Authors: ZUO LI
Keywords: transcription factor, transcription factor relations, text-mining, biomedical text, NLP, database
Issue Date: 25-Jun-2004
Source: ZUO LI (2004-06-25). Recognition of transcription factor interactions based on NLP and artificial intelligence. ScholarBank@NUS Repository.
Abstract: Transcription factors (TFs) are key activators of transcriptional regulation of all genes in gene transcriptional regulatory networks. Extracting a large volume of the existing knowledge about TF-TF relations from the biomedical literature is not well developed. In this thesis, we present a system called Dragon TF Relation Extractor (DTFRE), for extracting TF-TF relations from biomedical texts. This system is based on one of the largest TF-TF relation databases to date, called TFRD. DTFRE (http://research.i2r.a-star.edu.sg/DRAGON/TFRE/) is the first public system that can extract relations between TFs from PubMed abstracts submitted by users. The system performance achieves an overall precision of 93% and recall 82% for a selected group of relation expressions. This technology can efficiently support biologists and medical scientists to infer function of genes, paving way for a more focused and detailed follow-up research.
URI: http://scholarbank.nus.edu.sg/handle/10635/27709
Appears in Collections:Master's Theses (Open)

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