title: Inference of transcriptional regulatory network by bootstrapping patterns.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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%.
  
  
  BACKGROUND
  The software, training/test datasets and learned patterns are available at http://140.116.99.138/∼hcw0901/PubMedSearch.php.
authors: Institute of Information Management, National Cheng Kung University, Tainan, Taiwan.
authors: Hei-Chia
authors: HC
authors: Wang
authors: Yi-Hsiu
authors: YH
authors: Chen
authors: Hung-Yu
authors: HY
authors: Kao
authors: Shaw-Jenq
authors: SJ
authors: Tsai
mesh_terms: D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors