Inference of transcriptional regulatory network by bootstrapping patterns

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Motivation: Transcriptional regulatory networks, which consist of linkages between transcription factors (TF) and target genes (TGene), control the expression of a genome and play important roles in all aspects of an organism's life cycle. Accurate prediction of transcriptional regulatory networks is critical in providing useful information for biologists to determine what to do next. Currently, there is a substantial amount of fragmented gene regulation information described in the medical literature. However, current related text analysis methods designed to identify protein-protein interactions are not entirely suitable for finding transcriptional regulatory networks.Result: In this article, we propose an automatic regulatory network inference method that uses bootstrapping of description patterns to predict the relationship between a TF and its TGenes. The proposed method differs from other regulatory network generators in that it makes use of both positive and negative patterns for different vector combinations in a sentence. Moreover, the positive pattern learning process can be fully automatic. Furthermore, patterns for active and passive voice sentences are learned separately. The experiments use 609 HIF-1 expert-tagged articles from PubMed as the gold standard. The results show that the proposed method can automatically generate a predicted regulatory network for a transcription factor. Our system achieves an F-measure of 72.60%.

Original languageEnglish
Article numberbtr155
Pages (from-to)1422-1428
Number of pages7
JournalBioinformatics
Volume27
Issue number10
DOIs
Publication statusPublished - 2011 May 1

Fingerprint

Transcription factors
Gene Regulatory Networks
Regulatory Networks
Bootstrapping
Transcription Factors
Transcription Factor
Genes
Proteins
Gene expression
Life cycle
Life Cycle Stages
PubMed
Text Analysis
Gene Regulation
Learning
Protein-protein Interaction
Genome
Learning Process
Gene Expression
Gold

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

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Inference of transcriptional regulatory network by bootstrapping patterns. / Wang, Hei Chia; Chen, Yi HSiu; Kao, Hung Yu; Tsai, Shaw Jenq.

In: Bioinformatics, Vol. 27, No. 10, btr155, 01.05.2011, p. 1422-1428.

Research output: Contribution to journalArticle

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