Using positive and negative patterns to extract information from journal articles regarding the regulation of a target gene by a transcription factor

研究成果: Article

2 引文 (Scopus)

摘要

Gene regulation research concerns the regulatory relationship between transcription factors (TFs) and their target genes (TGenes). Due to the rapid acceleration of biological research, it is impractical for biologists to read all of the relevant literature and manually extract all of the information about the regulatory relationships between a TF and its TGenes. This paper proposes a method utilizing negative and positive textual patterns to extract regulatory information regarding certain TF-TGene pairs, which provides insightful information to biologists and saves them time from excessive literature reading. We hypothesized that the negative patterns could be used for filtering and that the system would mainly rely on the positive patterns to mine the regulatory TF-TGene relationships from the text. We also examined whether WordNet could be utilized to improve the pattern recognition performance. The results show that the negative pattern should be used for initial filtering, and then the positive patterns can extract information related to gene regulation. Moreover, WordNet seems to have little effect on the performance when extracting gene regulations.

原文English
頁(從 - 到)2214-2221
頁數8
期刊Computers in Biology and Medicine
43
發行號12
DOIs
出版狀態Published - 2013 十二月 1

指紋

Transcription factors
Transcription Factors
Genes
Gene expression
Pattern recognition
Research
Reading

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

引用此文

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title = "Using positive and negative patterns to extract information from journal articles regarding the regulation of a target gene by a transcription factor",
abstract = "Gene regulation research concerns the regulatory relationship between transcription factors (TFs) and their target genes (TGenes). Due to the rapid acceleration of biological research, it is impractical for biologists to read all of the relevant literature and manually extract all of the information about the regulatory relationships between a TF and its TGenes. This paper proposes a method utilizing negative and positive textual patterns to extract regulatory information regarding certain TF-TGene pairs, which provides insightful information to biologists and saves them time from excessive literature reading. We hypothesized that the negative patterns could be used for filtering and that the system would mainly rely on the positive patterns to mine the regulatory TF-TGene relationships from the text. We also examined whether WordNet could be utilized to improve the pattern recognition performance. The results show that the negative pattern should be used for initial filtering, and then the positive patterns can extract information related to gene regulation. Moreover, WordNet seems to have little effect on the performance when extracting gene regulations.",
author = "Hei-Chia Wang and Kooi, {Tock Kheng} and Hung-Yu Kao and Shih-Chieh Lin and Shaw-Jenq Tsai",
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AU - Wang, Hei-Chia

AU - Kooi, Tock Kheng

AU - Kao, Hung-Yu

AU - Lin, Shih-Chieh

AU - Tsai, Shaw-Jenq

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AB - Gene regulation research concerns the regulatory relationship between transcription factors (TFs) and their target genes (TGenes). Due to the rapid acceleration of biological research, it is impractical for biologists to read all of the relevant literature and manually extract all of the information about the regulatory relationships between a TF and its TGenes. This paper proposes a method utilizing negative and positive textual patterns to extract regulatory information regarding certain TF-TGene pairs, which provides insightful information to biologists and saves them time from excessive literature reading. We hypothesized that the negative patterns could be used for filtering and that the system would mainly rely on the positive patterns to mine the regulatory TF-TGene relationships from the text. We also examined whether WordNet could be utilized to improve the pattern recognition performance. The results show that the negative pattern should be used for initial filtering, and then the positive patterns can extract information related to gene regulation. Moreover, WordNet seems to have little effect on the performance when extracting gene regulations.

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