A semi-supervised, weighted pattern-learning approach for extraction of gene regulation relationships from scientific literature

Research output: Contribution to journalArticle

Abstract

Moreover, the large amount of textual knowledge in the existing biomedical literature is growing rapidly, and the creation of manual patterns from the available literature is becoming more difficult. There is an increasing demand to extract potential generic regulatory relationships from unlabelled data sets. In this paper, we describe a Semi-Supervised, Weighted Pattern Learning method (SSWPL) to extract such generic regulatory information from the literature. SSWPL can build new regulatory patterns according to predefined initial patterns from unlabelled data in the literature. These constructed regulatory patterns are then used to extract generic regulatory information from PubMed abstracts. The results presented herein demonstrate that our method can be utilised to effectively extract generic regulatory relationships from the literature by using learned, weighted patterns through semi-supervised pattern learning.

Original languageEnglish
Pages (from-to)401-416
Number of pages16
JournalInternational Journal of Data Mining and Bioinformatics
Volume9
Issue number4
DOIs
Publication statusPublished - 2014 Jan 1

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Literature
technical literature
Gene expression
Learning
regulation
Genes
learning
learning method
PubMed
literature
demand

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

Cite this

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title = "A semi-supervised, weighted pattern-learning approach for extraction of gene regulation relationships from scientific literature",
abstract = "Moreover, the large amount of textual knowledge in the existing biomedical literature is growing rapidly, and the creation of manual patterns from the available literature is becoming more difficult. There is an increasing demand to extract potential generic regulatory relationships from unlabelled data sets. In this paper, we describe a Semi-Supervised, Weighted Pattern Learning method (SSWPL) to extract such generic regulatory information from the literature. SSWPL can build new regulatory patterns according to predefined initial patterns from unlabelled data in the literature. These constructed regulatory patterns are then used to extract generic regulatory information from PubMed abstracts. The results presented herein demonstrate that our method can be utilised to effectively extract generic regulatory relationships from the literature by using learned, weighted patterns through semi-supervised pattern learning.",
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